import tensorflow as tf
from keras.layers.core import Dense, Dropout
from keras.layers import BatchNormalization
from keras.optimizers import Adam, SGD, Nadam
from keras.callbacks import ModelCheckpoint
from keras.models import Model, load_model
from tensorflow.keras.applications import densenet
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img, img_to_array
from keras import backend as K

import numpy as np
import os
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error

#Load data
nTest = 90
nPixels = 224

mds_360 = np.loadtxt("mds_360.txt")
categories = [i for i in range(30) for j in range(12)]

def load_images(directory, nPixels, preprocesser):
    X = []
    for subdir, dirs, files in os.walk(directory):
        for file in files:
            if file.endswith(".jpg"):
                img = load_img(os.path.join(subdir, file), target_size=(nPixels, nPixels))
                x = img_to_array(img)
                X.append(x)
    X = np.stack(X)
    X = preprocesser(X)
    return X

X = load_images("360 Rocks", nPixels, lambda x: densenet.preprocess_input(np.expand_dims(x, axis=0)).squeeze())
(X_train_, X_test, 
 Y_train_, Y_test, 
 categories_train_, categories_test) = train_test_split(X, 
                                                        mds_360, 
                                                        categories,
                                                        test_size=nTest,
                                                        stratify=categories, 
                                                        random_state=0)

(X_train, X_validate, 
 Y_train, Y_validate) = train_test_split(X_train_, 
                                         Y_train_, 
                                         test_size=nTest,
                                         stratify=categories_train_, 
                                         random_state=0)

X_120 = load_images("120 Rocks", nPixels, lambda x: densenet.preprocess_input(np.expand_dims(x, axis=0)).squeeze())
Y_120 = np.loadtxt("mds_120.txt")

datagen = ImageDataGenerator(featurewise_center=False,
                    samplewise_center=False,
                    featurewise_std_normalization=False,
                    samplewise_std_normalization=False,
                    zca_whitening=False,
                    rotation_range=20,
                    width_shift_range=0.2,
                    height_shift_range=0.2,
                    shear_range=0.2,
                    zoom_range=0.2,
                    channel_shift_range=0.,
                    fill_mode='nearest',
                    cval=0.,
                    horizontal_flip=True,
                    vertical_flip=True)

from keras.callbacks import Callback

class CustomEarlyStopping(Callback):
    def __init__(self, filepath, monitor='val_loss', patience=20, warmup_epochs=5):
        super(CustomEarlyStopping, self).__init__()
        self.filepath = filepath
        self.monitor = monitor
        self.patience = patience
        self.warmup_epochs = warmup_epochs
        self.best_weights = None
        self.wait = 0
        self.stopped_epoch = 0
        self.best_val_loss = float('inf')

    def on_epoch_end(self, epoch, logs=None):
        current_val_loss = logs.get(self.monitor)
        if current_val_loss is None:
            return
        
        if epoch < self.warmup_epochs:
            # If still in warmup phase, do not trigger early stopping
            return
        
        if current_val_loss < self.best_val_loss:
            self.best_val_loss = current_val_loss
            self.wait = 0
            # Save the weights of the best model
            self.best_weights = self.model.get_weights()
        else:
            self.wait += 1
            if self.wait >= self.patience:
                self.stopped_epoch = epoch
                self.model.set_weights(self.best_weights)  # Load best weights
                self.model.save(self.filepath)  # Save the best model to a file
                self.model.stop_training = True  # Stop training

    def on_train_end(self, logs=None):
        if self.stopped_epoch > 0:
            print(f"Epoch {self.stopped_epoch + 1}: early stopping")
        else:
            print("Training completed without early stopping")

Using Nadam Optimizer with He Normal Initialization with 500 Epochs and 2 - 256 Dense Layers for Finetuning

nDim = 8
nEpochs = 500
dropout = 0.5
nEnsemble = 10
          
nDense = 256
nLayers = 2
loglr = -2.2200654426745987

lr = 10 ** loglr

batch_size = 90

for e in range(nEnsemble):
    #Build model
    arch = densenet.DenseNet121(include_top=False, pooling='avg')
    for layer in arch.layers:
        layer.trainable = False    
    
    x = arch.output
    x = Dropout(dropout)(x)
    for lyr in range(nLayers):
        x = Dense(nDense, activation='relu', kernel_initializer='he_normal')(x)
        x = BatchNormalization()(x)
        x = Dropout(dropout)(x)
    x = Dense(nDim)(x)
    
    model = Model(inputs=arch.input, outputs=x)
    
    #Initial training
    model.compile(loss='mean_squared_error', optimizer=Nadam(learning_rate=lr))
    
    filepath = 'intermediate_model_densenet_500_256_2.hdf5'
    custom_early_stopping = CustomEarlyStopping(filepath, monitor='val_loss', patience=20, warmup_epochs=50)

    hist1 = model.fit(datagen.flow(X_train, Y_train, batch_size), 
                                steps_per_epoch=len(X_train) / batch_size,
                                epochs=nEpochs,
                                validation_data=(X_validate, Y_validate),
                                callbacks=[custom_early_stopping],
                                verbose=True)
    
    #Fine tuning
    model = load_model("intermediate_model_densenet_500_256_2.hdf5")
    
    for layer in model.layers:
        layer.trainable = True
    
    model.compile(optimizer=Nadam(learning_rate=0.0001), loss='mean_squared_error')
    
    batch_size = 30 #reduce the batch size so that the gradients of all layers can fit in memory
    
    filepath = 'ensemble_densenet_8_{}.hdf5'.format(e)
    custom_early_stopping = CustomEarlyStopping(filepath, monitor='val_loss', patience=20)
    
    hist2 = model.fit(datagen.flow(X_train, Y_train, batch_size), 
                                steps_per_epoch=len(X_train) / batch_size,
                                epochs=nEpochs,
                                validation_data=(X_validate, Y_validate),
                                callbacks=[custom_early_stopping],
                                verbose=True)
    K.clear_session() #Clear tensorflow session to prevent memory issues

Epoch 1/500
2/2 [==============================] - 11s 3s/step - loss: 9.8876 - val_loss: 6.7203
Epoch 2/500
2/2 [==============================] - 2s 1s/step - loss: 9.1201 - val_loss: 6.5210
Epoch 3/500
2/2 [==============================] - 2s 1s/step - loss: 7.6253 - val_loss: 6.5451
Epoch 4/500
2/2 [==============================] - 2s 1s/step - loss: 7.1695 - val_loss: 6.9089
Epoch 5/500
2/2 [==============================] - 2s 1s/step - loss: 6.7570 - val_loss: 7.0188
Epoch 6/500
2/2 [==============================] - 2s 1s/step - loss: 6.1269 - val_loss: 7.5409
Epoch 7/500
2/2 [==============================] - 2s 1s/step - loss: 5.8250 - val_loss: 7.2855
Epoch 8/500
2/2 [==============================] - 2s 1s/step - loss: 5.5841 - val_loss: 8.0342
Epoch 9/500
2/2 [==============================] - 2s 1s/step - loss: 5.3348 - val_loss: 8.3533
Epoch 10/500
2/2 [==============================] - 2s 1s/step - loss: 4.9806 - val_loss: 8.1148
Epoch 11/500
2/2 [==============================] - 2s 1s/step - loss: 4.8408 - val_loss: 7.8814
Epoch 12/500
2/2 [==============================] - 2s 1s/step - loss: 4.6797 - val_loss: 6.1779
Epoch 13/500
2/2 [==============================] - 2s 1s/step - loss: 4.6326 - val_loss: 5.9979
Epoch 14/500
2/2 [==============================] - 2s 1s/step - loss: 4.4477 - val_loss: 5.7529
Epoch 15/500
2/2 [==============================] - 2s 1s/step - loss: 4.2265 - val_loss: 6.0981
Epoch 16/500
2/2 [==============================] - 2s 1s/step - loss: 4.3330 - val_loss: 5.9041
Epoch 17/500
2/2 [==============================] - 2s 1s/step - loss: 3.8968 - val_loss: 6.1827
Epoch 18/500
2/2 [==============================] - 2s 1s/step - loss: 3.9614 - val_loss: 6.2283
Epoch 19/500
2/2 [==============================] - 2s 1s/step - loss: 3.8383 - val_loss: 5.6413
Epoch 20/500
2/2 [==============================] - 2s 1s/step - loss: 3.7839 - val_loss: 5.1788
Epoch 21/500
2/2 [==============================] - 2s 1s/step - loss: 3.8042 - val_loss: 5.0101
Epoch 22/500
2/2 [==============================] - 2s 1s/step - loss: 3.7784 - val_loss: 5.3462
Epoch 23/500
2/2 [==============================] - 2s 1s/step - loss: 3.6717 - val_loss: 4.9919
Epoch 24/500
2/2 [==============================] - 2s 1s/step - loss: 3.4442 - val_loss: 4.6811
Epoch 25/500
2/2 [==============================] - 2s 1s/step - loss: 3.3765 - val_loss: 4.4233
Epoch 26/500
2/2 [==============================] - 2s 1s/step - loss: 3.4121 - val_loss: 4.3387
Epoch 27/500
2/2 [==============================] - 2s 1s/step - loss: 3.2221 - val_loss: 4.0075
Epoch 28/500
2/2 [==============================] - 2s 1s/step - loss: 3.3416 - val_loss: 3.9367
Epoch 29/500
2/2 [==============================] - 2s 1s/step - loss: 3.2523 - val_loss: 3.9308
Epoch 30/500
2/2 [==============================] - 2s 1s/step - loss: 3.1732 - val_loss: 4.0979
Epoch 31/500
2/2 [==============================] - 2s 1s/step - loss: 3.3929 - val_loss: 3.7054
Epoch 32/500
2/2 [==============================] - 2s 1s/step - loss: 3.0056 - val_loss: 3.7024
Epoch 33/500
2/2 [==============================] - 2s 1s/step - loss: 3.2407 - val_loss: 3.6322
Epoch 34/500
2/2 [==============================] - 2s 1s/step - loss: 3.0613 - val_loss: 3.5949
Epoch 35/500
2/2 [==============================] - 2s 1s/step - loss: 2.9829 - val_loss: 3.6115
Epoch 36/500
2/2 [==============================] - 2s 1s/step - loss: 3.0758 - val_loss: 3.6988
Epoch 37/500
2/2 [==============================] - 2s 1s/step - loss: 2.9311 - val_loss: 3.5999
Epoch 38/500
2/2 [==============================] - 2s 1s/step - loss: 3.0015 - val_loss: 3.5074
Epoch 39/500
2/2 [==============================] - 2s 1s/step - loss: 2.9840 - val_loss: 3.3224
Epoch 40/500
2/2 [==============================] - 2s 1s/step - loss: 2.9746 - val_loss: 3.2716
Epoch 41/500
2/2 [==============================] - 2s 1s/step - loss: 2.9238 - val_loss: 3.1033
Epoch 42/500
2/2 [==============================] - 2s 1s/step - loss: 2.8859 - val_loss: 3.1538
Epoch 43/500
2/2 [==============================] - 2s 1s/step - loss: 2.7876 - val_loss: 3.0500
Epoch 44/500
2/2 [==============================] - 2s 1s/step - loss: 2.7502 - val_loss: 3.0135
Epoch 45/500
2/2 [==============================] - 2s 1s/step - loss: 2.8434 - val_loss: 3.0481
Epoch 46/500
2/2 [==============================] - 2s 1s/step - loss: 2.7447 - val_loss: 3.0670
Epoch 47/500
2/2 [==============================] - 2s 1s/step - loss: 2.6469 - val_loss: 2.9782
Epoch 48/500
2/2 [==============================] - 2s 1s/step - loss: 2.7360 - val_loss: 2.7679
Epoch 49/500
2/2 [==============================] - 2s 1s/step - loss: 2.4993 - val_loss: 2.7643
Epoch 50/500
2/2 [==============================] - 2s 1s/step - loss: 2.8166 - val_loss: 2.6442
Epoch 51/500
2/2 [==============================] - 2s 1s/step - loss: 2.7725 - val_loss: 2.6729
Epoch 52/500
2/2 [==============================] - 2s 1s/step - loss: 2.5818 - val_loss: 2.7519
Epoch 53/500
2/2 [==============================] - 2s 1s/step - loss: 2.7116 - val_loss: 2.7584
Epoch 54/500
2/2 [==============================] - 2s 1s/step - loss: 2.7068 - val_loss: 2.7133
Epoch 55/500
2/2 [==============================] - 2s 1s/step - loss: 2.5763 - val_loss: 2.6509
Epoch 56/500
2/2 [==============================] - 2s 1s/step - loss: 2.7274 - val_loss: 2.6626
Epoch 57/500
2/2 [==============================] - 2s 1s/step - loss: 2.6448 - val_loss: 2.5227
Epoch 58/500
2/2 [==============================] - 2s 1s/step - loss: 2.5801 - val_loss: 2.5245
Epoch 59/500
2/2 [==============================] - 2s 1s/step - loss: 2.6952 - val_loss: 2.4284
Epoch 60/500
2/2 [==============================] - 2s 1s/step - loss: 2.5499 - val_loss: 2.3811
Epoch 61/500
2/2 [==============================] - 2s 1s/step - loss: 2.7612 - val_loss: 2.4189
Epoch 62/500
2/2 [==============================] - 2s 1s/step - loss: 2.6554 - val_loss: 2.4126
Epoch 63/500
2/2 [==============================] - 2s 1s/step - loss: 2.4961 - val_loss: 2.4235
Epoch 64/500
2/2 [==============================] - 2s 1s/step - loss: 2.4933 - val_loss: 2.3651
Epoch 65/500
2/2 [==============================] - 3s 1s/step - loss: 2.6575 - val_loss: 2.4129
Epoch 66/500
2/2 [==============================] - 2s 1s/step - loss: 2.6261 - val_loss: 2.4089
Epoch 67/500
2/2 [==============================] - 2s 1s/step - loss: 2.5414 - val_loss: 2.3941
Epoch 68/500
2/2 [==============================] - 2s 1s/step - loss: 2.5936 - val_loss: 2.3843
Epoch 69/500
2/2 [==============================] - 3s 1s/step - loss: 2.4665 - val_loss: 2.3144
Epoch 70/500
2/2 [==============================] - 3s 1s/step - loss: 2.6269 - val_loss: 2.3091
Epoch 71/500
2/2 [==============================] - 2s 1s/step - loss: 2.7151 - val_loss: 2.3713
Epoch 72/500
2/2 [==============================] - 2s 1s/step - loss: 2.4273 - val_loss: 2.2955
Epoch 73/500
2/2 [==============================] - 2s 1s/step - loss: 2.4347 - val_loss: 2.2411
Epoch 74/500
2/2 [==============================] - 3s 1s/step - loss: 2.4052 - val_loss: 2.2130
Epoch 75/500
2/2 [==============================] - 2s 1s/step - loss: 2.4492 - val_loss: 2.2198
Epoch 76/500
2/2 [==============================] - 2s 1s/step - loss: 2.4417 - val_loss: 2.2499
Epoch 77/500
2/2 [==============================] - 2s 1s/step - loss: 2.6880 - val_loss: 2.2494
Epoch 78/500
2/2 [==============================] - 2s 1s/step - loss: 2.6203 - val_loss: 2.1961
Epoch 79/500
2/2 [==============================] - 2s 1s/step - loss: 2.5055 - val_loss: 2.1587
Epoch 80/500
2/2 [==============================] - 2s 1s/step - loss: 2.6379 - val_loss: 2.1593
Epoch 81/500
2/2 [==============================] - 2s 1s/step - loss: 2.3890 - val_loss: 2.1611
Epoch 82/500
2/2 [==============================] - 2s 1s/step - loss: 2.4643 - val_loss: 2.1857
Epoch 83/500
2/2 [==============================] - 2s 1s/step - loss: 2.4571 - val_loss: 2.1829
Epoch 84/500
2/2 [==============================] - 2s 1s/step - loss: 2.5958 - val_loss: 2.2314
Epoch 85/500
2/2 [==============================] - 2s 1s/step - loss: 2.5712 - val_loss: 2.2705
Epoch 86/500
2/2 [==============================] - 2s 1s/step - loss: 2.3978 - val_loss: 2.2509
Epoch 87/500
2/2 [==============================] - 2s 1s/step - loss: 2.3232 - val_loss: 2.2174
Epoch 88/500
2/2 [==============================] - 2s 1s/step - loss: 2.3527 - val_loss: 2.2123
Epoch 89/500
2/2 [==============================] - 2s 1s/step - loss: 2.5500 - val_loss: 2.2253
Epoch 90/500
2/2 [==============================] - 2s 1s/step - loss: 2.4930 - val_loss: 2.1736
Epoch 91/500
2/2 [==============================] - 2s 1s/step - loss: 2.4278 - val_loss: 2.1663
Epoch 92/500
2/2 [==============================] - 2s 1s/step - loss: 2.3348 - val_loss: 2.1854
Epoch 93/500
2/2 [==============================] - 2s 1s/step - loss: 2.4526 - val_loss: 2.1720
Epoch 94/500
2/2 [==============================] - 2s 1s/step - loss: 2.4369 - val_loss: 2.1525
Epoch 95/500
2/2 [==============================] - 2s 1s/step - loss: 2.3798 - val_loss: 2.1712
Epoch 96/500
2/2 [==============================] - 2s 1s/step - loss: 2.2422 - val_loss: 2.1950
Epoch 97/500
2/2 [==============================] - 2s 1s/step - loss: 2.3802 - val_loss: 2.1858
Epoch 98/500
2/2 [==============================] - 2s 1s/step - loss: 2.3421 - val_loss: 2.1734
Epoch 99/500
2/2 [==============================] - 2s 1s/step - loss: 2.4127 - val_loss: 2.1725
Epoch 100/500
2/2 [==============================] - 2s 1s/step - loss: 2.5251 - val_loss: 2.1703
Epoch 101/500
2/2 [==============================] - 2s 1s/step - loss: 2.4289 - val_loss: 2.1055
Epoch 102/500
2/2 [==============================] - 2s 1s/step - loss: 2.2184 - val_loss: 2.1307
Epoch 103/500
2/2 [==============================] - 2s 1s/step - loss: 2.3370 - val_loss: 2.1139
Epoch 104/500
2/2 [==============================] - 2s 1s/step - loss: 2.3771 - val_loss: 2.1215
Epoch 105/500
2/2 [==============================] - 2s 1s/step - loss: 2.2837 - val_loss: 2.1227
Epoch 106/500
2/2 [==============================] - 2s 1s/step - loss: 2.4465 - val_loss: 2.1591
Epoch 107/500
2/2 [==============================] - 2s 1s/step - loss: 2.2460 - val_loss: 2.2001
Epoch 108/500
2/2 [==============================] - 2s 1s/step - loss: 2.4187 - val_loss: 2.1326
Epoch 109/500
2/2 [==============================] - 2s 1s/step - loss: 2.2078 - val_loss: 2.0857
Epoch 110/500
2/2 [==============================] - 2s 1s/step - loss: 2.3261 - val_loss: 2.0776
Epoch 111/500
2/2 [==============================] - 2s 1s/step - loss: 2.1122 - val_loss: 2.1018
Epoch 112/500
2/2 [==============================] - 2s 1s/step - loss: 2.2437 - val_loss: 2.0927
Epoch 113/500
2/2 [==============================] - 2s 1s/step - loss: 2.2254 - val_loss: 2.1330
Epoch 114/500
2/2 [==============================] - 2s 1s/step - loss: 2.2168 - val_loss: 2.1278
Epoch 115/500
2/2 [==============================] - 2s 1s/step - loss: 2.5092 - val_loss: 2.1548
Epoch 116/500
2/2 [==============================] - 2s 1s/step - loss: 2.3083 - val_loss: 2.1718
Epoch 117/500
2/2 [==============================] - 2s 1s/step - loss: 2.1969 - val_loss: 2.1931
Epoch 118/500
2/2 [==============================] - 2s 1s/step - loss: 2.2925 - val_loss: 2.1866
Epoch 119/500
2/2 [==============================] - 2s 1s/step - loss: 2.2946 - val_loss: 2.2156
Epoch 120/500
2/2 [==============================] - 2s 1s/step - loss: 2.1715 - val_loss: 2.1717
Epoch 121/500
2/2 [==============================] - 2s 1s/step - loss: 2.2279 - val_loss: 2.1322
Epoch 122/500
2/2 [==============================] - 2s 1s/step - loss: 2.2047 - val_loss: 2.1329
Epoch 123/500
2/2 [==============================] - 2s 1s/step - loss: 2.3536 - val_loss: 2.1313
Epoch 124/500
2/2 [==============================] - 2s 1s/step - loss: 2.2859 - val_loss: 2.1349
Epoch 125/500
2/2 [==============================] - 2s 1s/step - loss: 2.3044 - val_loss: 2.1103
Epoch 126/500
2/2 [==============================] - 2s 1s/step - loss: 2.2274 - val_loss: 2.0960
Epoch 127/500
2/2 [==============================] - 2s 1s/step - loss: 2.3254 - val_loss: 2.0679
Epoch 128/500
2/2 [==============================] - 2s 1s/step - loss: 2.2295 - val_loss: 2.0613
Epoch 129/500
2/2 [==============================] - 2s 1s/step - loss: 2.2567 - val_loss: 2.0961
Epoch 130/500
2/2 [==============================] - 2s 1s/step - loss: 2.2248 - val_loss: 2.1020
Epoch 131/500
2/2 [==============================] - 2s 1s/step - loss: 2.3120 - val_loss: 2.0875
Epoch 132/500
2/2 [==============================] - 2s 1s/step - loss: 2.1721 - val_loss: 2.0671
Epoch 133/500
2/2 [==============================] - 2s 1s/step - loss: 2.2330 - val_loss: 2.0250
Epoch 134/500
2/2 [==============================] - 2s 1s/step - loss: 2.2384 - val_loss: 2.0041
Epoch 135/500
2/2 [==============================] - 2s 1s/step - loss: 2.4626 - val_loss: 2.0028
Epoch 136/500
2/2 [==============================] - 2s 1s/step - loss: 2.2308 - val_loss: 1.9807
Epoch 137/500
2/2 [==============================] - 2s 1s/step - loss: 2.1524 - val_loss: 1.9537
Epoch 138/500
2/2 [==============================] - 2s 1s/step - loss: 2.1594 - val_loss: 1.9875
Epoch 139/500
2/2 [==============================] - 2s 1s/step - loss: 2.2939 - val_loss: 1.9529
Epoch 140/500
2/2 [==============================] - 2s 1s/step - loss: 2.3088 - val_loss: 1.9900
Epoch 141/500
2/2 [==============================] - 2s 1s/step - loss: 2.0643 - val_loss: 2.0214
Epoch 142/500
2/2 [==============================] - 2s 1s/step - loss: 2.2581 - val_loss: 2.0490
Epoch 143/500
2/2 [==============================] - 2s 1s/step - loss: 2.2208 - val_loss: 2.0266
Epoch 144/500
2/2 [==============================] - 2s 1s/step - loss: 2.1797 - val_loss: 2.0536
Epoch 145/500
2/2 [==============================] - 2s 1s/step - loss: 2.3738 - val_loss: 2.0444
Epoch 146/500
2/2 [==============================] - 2s 1s/step - loss: 2.1871 - val_loss: 2.0374
Epoch 147/500
2/2 [==============================] - 2s 1s/step - loss: 2.1942 - val_loss: 2.0384
Epoch 148/500
2/2 [==============================] - 2s 1s/step - loss: 2.3258 - val_loss: 1.9961
Epoch 149/500
2/2 [==============================] - 2s 1s/step - loss: 2.1712 - val_loss: 1.9505
Epoch 150/500
2/2 [==============================] - 2s 1s/step - loss: 2.3734 - val_loss: 1.9826
Epoch 151/500
2/2 [==============================] - 2s 1s/step - loss: 2.2786 - val_loss: 1.9588
Epoch 152/500
2/2 [==============================] - 2s 1s/step - loss: 2.1934 - val_loss: 1.9762
Epoch 153/500
2/2 [==============================] - 2s 1s/step - loss: 2.1712 - val_loss: 1.9921
Epoch 154/500
2/2 [==============================] - 2s 1s/step - loss: 2.1263 - val_loss: 1.9915
Epoch 155/500
2/2 [==============================] - 2s 1s/step - loss: 2.3680 - val_loss: 1.9857
Epoch 156/500
2/2 [==============================] - 2s 1s/step - loss: 2.1209 - val_loss: 1.9903
Epoch 157/500
2/2 [==============================] - 2s 1s/step - loss: 2.1403 - val_loss: 1.9824
Epoch 158/500
2/2 [==============================] - 2s 1s/step - loss: 2.1296 - val_loss: 1.9917
Epoch 159/500
2/2 [==============================] - 2s 1s/step - loss: 2.0997 - val_loss: 1.9922
Epoch 160/500
2/2 [==============================] - 2s 1s/step - loss: 2.1667 - val_loss: 2.0005
Epoch 161/500
2/2 [==============================] - 2s 1s/step - loss: 2.1397 - val_loss: 2.0044
Epoch 162/500
2/2 [==============================] - 2s 1s/step - loss: 2.0623 - val_loss: 1.9753
Epoch 163/500
2/2 [==============================] - 2s 1s/step - loss: 2.0442 - val_loss: 1.9606
Epoch 164/500
2/2 [==============================] - 2s 1s/step - loss: 2.0807 - val_loss: 1.9689
Epoch 165/500
2/2 [==============================] - 2s 1s/step - loss: 2.1479 - val_loss: 1.9738
Epoch 166/500
2/2 [==============================] - 2s 1s/step - loss: 2.1212 - val_loss: 1.9840
Epoch 167/500
2/2 [==============================] - 2s 1s/step - loss: 2.2002 - val_loss: 1.9763
Epoch 168/500
2/2 [==============================] - 2s 1s/step - loss: 2.1082 - val_loss: 1.9784
Epoch 169/500
2/2 [==============================] - 3s 2s/step - loss: 2.1880 - val_loss: 1.9558
Epoch 169: early stopping
Epoch 1/500
6/6 [==============================] - 36s 758ms/step - loss: 3.6916 - val_loss: 2.0184
Epoch 2/500
6/6 [==============================] - 3s 409ms/step - loss: 3.1390 - val_loss: 2.2699
Epoch 3/500
6/6 [==============================] - 3s 408ms/step - loss: 2.6015 - val_loss: 2.3716
Epoch 4/500
6/6 [==============================] - 3s 412ms/step - loss: 2.5459 - val_loss: 2.4933
Epoch 5/500
6/6 [==============================] - 3s 400ms/step - loss: 2.2372 - val_loss: 2.4185
Epoch 6/500
6/6 [==============================] - 3s 458ms/step - loss: 2.2382 - val_loss: 2.4953
Epoch 7/500
6/6 [==============================] - 3s 439ms/step - loss: 2.0820 - val_loss: 2.3434
Epoch 8/500
6/6 [==============================] - 3s 405ms/step - loss: 1.9963 - val_loss: 2.3684
Epoch 9/500
6/6 [==============================] - 3s 436ms/step - loss: 2.0033 - val_loss: 2.3100
Epoch 10/500
6/6 [==============================] - 3s 444ms/step - loss: 1.8193 - val_loss: 2.2781
Epoch 11/500
6/6 [==============================] - 3s 393ms/step - loss: 2.1569 - val_loss: 2.3323
Epoch 12/500
6/6 [==============================] - 3s 402ms/step - loss: 1.9054 - val_loss: 2.3388
Epoch 13/500
6/6 [==============================] - 3s 431ms/step - loss: 1.9171 - val_loss: 2.2572
Epoch 14/500
6/6 [==============================] - 3s 433ms/step - loss: 1.7577 - val_loss: 2.2205
Epoch 15/500
6/6 [==============================] - 3s 446ms/step - loss: 1.8822 - val_loss: 2.1150
Epoch 16/500
6/6 [==============================] - 3s 454ms/step - loss: 1.7802 - val_loss: 2.0754
Epoch 17/500
6/6 [==============================] - 3s 411ms/step - loss: 1.8397 - val_loss: 2.1216
Epoch 18/500
6/6 [==============================] - 3s 386ms/step - loss: 1.6974 - val_loss: 2.1003
Epoch 19/500
6/6 [==============================] - 3s 443ms/step - loss: 1.7072 - val_loss: 2.0668
Epoch 20/500
6/6 [==============================] - 3s 440ms/step - loss: 1.6892 - val_loss: 1.9622
Epoch 21/500
6/6 [==============================] - 3s 439ms/step - loss: 1.7979 - val_loss: 1.8916
Epoch 22/500
6/6 [==============================] - 3s 434ms/step - loss: 1.6780 - val_loss: 1.7991
Epoch 23/500
6/6 [==============================] - 3s 438ms/step - loss: 1.6842 - val_loss: 1.7753
Epoch 24/500
6/6 [==============================] - 3s 447ms/step - loss: 1.6348 - val_loss: 1.7696
Epoch 25/500
6/6 [==============================] - 3s 400ms/step - loss: 1.5309 - val_loss: 1.7783
Epoch 26/500
6/6 [==============================] - 3s 446ms/step - loss: 1.6884 - val_loss: 1.7398
Epoch 27/500
6/6 [==============================] - 3s 405ms/step - loss: 1.5727 - val_loss: 1.7636
Epoch 28/500
6/6 [==============================] - 3s 411ms/step - loss: 1.6291 - val_loss: 1.8339
Epoch 29/500
6/6 [==============================] - 3s 409ms/step - loss: 1.5157 - val_loss: 1.8029
Epoch 30/500
6/6 [==============================] - 3s 415ms/step - loss: 1.5321 - val_loss: 1.8441
Epoch 31/500
6/6 [==============================] - 3s 418ms/step - loss: 1.5158 - val_loss: 1.7767
Epoch 32/500
6/6 [==============================] - 3s 452ms/step - loss: 1.5844 - val_loss: 1.7176
Epoch 33/500
6/6 [==============================] - 3s 392ms/step - loss: 1.4848 - val_loss: 1.7418
Epoch 34/500
6/6 [==============================] - 3s 432ms/step - loss: 1.5578 - val_loss: 1.6808
Epoch 35/500
6/6 [==============================] - 3s 424ms/step - loss: 1.5037 - val_loss: 1.6636
Epoch 36/500
6/6 [==============================] - 3s 424ms/step - loss: 1.4213 - val_loss: 1.6235
Epoch 37/500
6/6 [==============================] - 3s 430ms/step - loss: 1.6067 - val_loss: 1.6209
Epoch 38/500
6/6 [==============================] - 3s 427ms/step - loss: 1.4737 - val_loss: 1.6126
Epoch 39/500
6/6 [==============================] - 3s 436ms/step - loss: 1.6120 - val_loss: 1.5974
Epoch 40/500
6/6 [==============================] - 3s 440ms/step - loss: 1.5023 - val_loss: 1.5865
Epoch 41/500
6/6 [==============================] - 3s 412ms/step - loss: 1.6382 - val_loss: 1.5868
Epoch 42/500
6/6 [==============================] - 3s 433ms/step - loss: 1.4188 - val_loss: 1.5428
Epoch 43/500
6/6 [==============================] - 3s 452ms/step - loss: 1.4196 - val_loss: 1.5377
Epoch 44/500
6/6 [==============================] - 3s 395ms/step - loss: 1.4142 - val_loss: 1.5837
Epoch 45/500
6/6 [==============================] - 3s 402ms/step - loss: 1.4616 - val_loss: 1.6209
Epoch 46/500
6/6 [==============================] - 3s 417ms/step - loss: 1.4728 - val_loss: 1.6340
Epoch 47/500
6/6 [==============================] - 3s 411ms/step - loss: 1.3579 - val_loss: 1.6397
Epoch 48/500
6/6 [==============================] - 3s 420ms/step - loss: 1.3440 - val_loss: 1.6454
Epoch 49/500
6/6 [==============================] - 3s 414ms/step - loss: 1.3904 - val_loss: 1.6455
Epoch 50/500
6/6 [==============================] - 3s 424ms/step - loss: 1.3183 - val_loss: 1.5469
Epoch 51/500
6/6 [==============================] - 3s 414ms/step - loss: 1.5033 - val_loss: 1.5429
Epoch 52/500
6/6 [==============================] - 3s 434ms/step - loss: 1.4050 - val_loss: 1.5641
Epoch 53/500
6/6 [==============================] - 3s 423ms/step - loss: 1.4613 - val_loss: 1.5865
Epoch 54/500
6/6 [==============================] - 3s 424ms/step - loss: 1.3468 - val_loss: 1.5753
Epoch 55/500
6/6 [==============================] - 3s 424ms/step - loss: 1.3977 - val_loss: 1.5625
Epoch 56/500
6/6 [==============================] - 3s 472ms/step - loss: 1.4229 - val_loss: 1.5096
Epoch 57/500
6/6 [==============================] - 3s 457ms/step - loss: 1.3600 - val_loss: 1.4931
Epoch 58/500
6/6 [==============================] - 3s 457ms/step - loss: 1.3895 - val_loss: 1.4901
Epoch 59/500
6/6 [==============================] - 3s 453ms/step - loss: 1.4687 - val_loss: 1.4692
Epoch 60/500
6/6 [==============================] - 3s 458ms/step - loss: 1.3516 - val_loss: 1.4536
Epoch 61/500
6/6 [==============================] - 3s 402ms/step - loss: 1.3413 - val_loss: 1.4979
Epoch 62/500
6/6 [==============================] - 3s 415ms/step - loss: 1.2828 - val_loss: 1.5189
Epoch 63/500
6/6 [==============================] - 3s 437ms/step - loss: 1.3626 - val_loss: 1.5435
Epoch 64/500
6/6 [==============================] - 3s 433ms/step - loss: 1.3268 - val_loss: 1.5548
Epoch 65/500
6/6 [==============================] - 3s 405ms/step - loss: 1.3225 - val_loss: 1.5314
Epoch 66/500
6/6 [==============================] - 3s 432ms/step - loss: 1.4576 - val_loss: 1.5516
Epoch 67/500
6/6 [==============================] - 3s 438ms/step - loss: 1.3549 - val_loss: 1.5363
Epoch 68/500
6/6 [==============================] - 3s 443ms/step - loss: 1.3282 - val_loss: 1.4837
Epoch 69/500
6/6 [==============================] - 3s 427ms/step - loss: 1.3426 - val_loss: 1.5037
Epoch 70/500
6/6 [==============================] - 3s 453ms/step - loss: 1.3526 - val_loss: 1.4961
Epoch 71/500
6/6 [==============================] - 3s 451ms/step - loss: 1.2116 - val_loss: 1.4919
Epoch 72/500
6/6 [==============================] - 3s 436ms/step - loss: 1.3261 - val_loss: 1.4801
Epoch 73/500
6/6 [==============================] - 3s 436ms/step - loss: 1.2960 - val_loss: 1.4785
Epoch 74/500
6/6 [==============================] - 3s 428ms/step - loss: 1.3048 - val_loss: 1.4547
Epoch 75/500
6/6 [==============================] - 3s 474ms/step - loss: 1.3043 - val_loss: 1.4138
Epoch 76/500
6/6 [==============================] - 3s 416ms/step - loss: 1.4071 - val_loss: 1.4355
Epoch 77/500
6/6 [==============================] - 3s 443ms/step - loss: 1.2346 - val_loss: 1.4851
Epoch 78/500
6/6 [==============================] - 3s 427ms/step - loss: 1.2736 - val_loss: 1.4924
Epoch 79/500
6/6 [==============================] - 3s 428ms/step - loss: 1.3565 - val_loss: 1.5319
Epoch 80/500
6/6 [==============================] - 3s 457ms/step - loss: 1.3338 - val_loss: 1.4918
Epoch 81/500
6/6 [==============================] - 3s 415ms/step - loss: 1.3533 - val_loss: 1.5079
Epoch 82/500
6/6 [==============================] - 3s 443ms/step - loss: 1.2923 - val_loss: 1.5250
Epoch 83/500
6/6 [==============================] - 3s 437ms/step - loss: 1.2357 - val_loss: 1.5097
Epoch 84/500
6/6 [==============================] - 3s 431ms/step - loss: 1.2823 - val_loss: 1.4535
Epoch 85/500
6/6 [==============================] - 3s 450ms/step - loss: 1.3123 - val_loss: 1.4418
Epoch 86/500
6/6 [==============================] - 3s 449ms/step - loss: 1.3407 - val_loss: 1.4526
Epoch 87/500
6/6 [==============================] - 3s 446ms/step - loss: 1.3136 - val_loss: 1.4385
Epoch 88/500
6/6 [==============================] - 3s 460ms/step - loss: 1.2519 - val_loss: 1.4810
Epoch 89/500
6/6 [==============================] - 3s 456ms/step - loss: 1.3879 - val_loss: 1.4750
Epoch 90/500
6/6 [==============================] - 3s 450ms/step - loss: 1.2403 - val_loss: 1.4990
Epoch 91/500
6/6 [==============================] - 3s 445ms/step - loss: 1.2871 - val_loss: 1.4898
Epoch 92/500
6/6 [==============================] - 3s 446ms/step - loss: 1.3587 - val_loss: 1.4629
Epoch 93/500
6/6 [==============================] - 3s 464ms/step - loss: 1.3669 - val_loss: 1.4433
Epoch 94/500
6/6 [==============================] - 3s 445ms/step - loss: 1.2114 - val_loss: 1.4367
Epoch 95/500
6/6 [==============================] - 4s 728ms/step - loss: 1.3140 - val_loss: 1.4876
Epoch 95: early stopping
Epoch 1/500
6/6 [==============================] - 11s 706ms/step - loss: 9.4592 - val_loss: 8.1828
Epoch 2/500
6/6 [==============================] - 2s 383ms/step - loss: 8.1427 - val_loss: 9.2355
Epoch 3/500
6/6 [==============================] - 2s 367ms/step - loss: 6.8648 - val_loss: 14.7044
Epoch 4/500
6/6 [==============================] - 2s 370ms/step - loss: 6.0209 - val_loss: 12.3670
Epoch 5/500
6/6 [==============================] - 2s 370ms/step - loss: 5.7634 - val_loss: 6.0251
Epoch 6/500
6/6 [==============================] - 2s 373ms/step - loss: 5.4531 - val_loss: 6.0532
Epoch 7/500
6/6 [==============================] - 2s 367ms/step - loss: 5.1288 - val_loss: 4.5812
Epoch 8/500
6/6 [==============================] - 2s 367ms/step - loss: 4.6331 - val_loss: 5.1384
Epoch 9/500
6/6 [==============================] - 2s 361ms/step - loss: 4.6085 - val_loss: 5.4484
Epoch 10/500
6/6 [==============================] - 2s 364ms/step - loss: 4.2773 - val_loss: 4.3555
Epoch 11/500
6/6 [==============================] - 2s 361ms/step - loss: 4.0349 - val_loss: 3.6879
Epoch 12/500
6/6 [==============================] - 2s 367ms/step - loss: 4.1921 - val_loss: 3.4921
Epoch 13/500
6/6 [==============================] - 2s 364ms/step - loss: 3.9047 - val_loss: 3.3548
Epoch 14/500
6/6 [==============================] - 2s 371ms/step - loss: 3.6502 - val_loss: 3.3595
Epoch 15/500
6/6 [==============================] - 2s 367ms/step - loss: 3.8730 - val_loss: 3.0405
Epoch 16/500
6/6 [==============================] - 2s 351ms/step - loss: 3.4981 - val_loss: 2.8427
Epoch 17/500
6/6 [==============================] - 2s 355ms/step - loss: 3.4857 - val_loss: 2.8634
Epoch 18/500
6/6 [==============================] - 2s 358ms/step - loss: 3.4267 - val_loss: 2.8970
Epoch 19/500
6/6 [==============================] - 2s 370ms/step - loss: 3.4113 - val_loss: 3.0490
Epoch 20/500
6/6 [==============================] - 2s 370ms/step - loss: 3.2847 - val_loss: 2.8128
Epoch 21/500
6/6 [==============================] - 2s 364ms/step - loss: 3.3355 - val_loss: 3.0361
Epoch 22/500
6/6 [==============================] - 2s 367ms/step - loss: 3.4238 - val_loss: 2.9027
Epoch 23/500
6/6 [==============================] - 2s 364ms/step - loss: 3.3431 - val_loss: 2.6797
Epoch 24/500
6/6 [==============================] - 2s 364ms/step - loss: 3.3073 - val_loss: 2.6889
Epoch 25/500
6/6 [==============================] - 2s 370ms/step - loss: 3.1897 - val_loss: 2.8697
Epoch 26/500
6/6 [==============================] - 2s 367ms/step - loss: 3.3068 - val_loss: 2.7449
Epoch 27/500
6/6 [==============================] - 2s 375ms/step - loss: 3.2581 - val_loss: 2.5826
Epoch 28/500
6/6 [==============================] - 2s 367ms/step - loss: 3.1058 - val_loss: 2.6928
Epoch 29/500
6/6 [==============================] - 2s 386ms/step - loss: 3.1027 - val_loss: 2.6253
Epoch 30/500
6/6 [==============================] - 2s 370ms/step - loss: 2.8418 - val_loss: 2.5570
Epoch 31/500
6/6 [==============================] - 2s 361ms/step - loss: 3.0834 - val_loss: 2.6056
Epoch 32/500
6/6 [==============================] - 2s 364ms/step - loss: 3.1663 - val_loss: 2.5153
Epoch 33/500
6/6 [==============================] - 2s 364ms/step - loss: 2.9905 - val_loss: 2.5577
Epoch 34/500
6/6 [==============================] - 2s 367ms/step - loss: 2.9280 - val_loss: 2.6539
Epoch 35/500
6/6 [==============================] - 2s 369ms/step - loss: 3.1143 - val_loss: 2.8124
Epoch 36/500
6/6 [==============================] - 2s 371ms/step - loss: 2.9779 - val_loss: 2.6898
Epoch 37/500
6/6 [==============================] - 2s 371ms/step - loss: 2.8546 - val_loss: 2.5913
Epoch 38/500
6/6 [==============================] - 2s 374ms/step - loss: 3.0559 - val_loss: 2.4889
Epoch 39/500
6/6 [==============================] - 2s 361ms/step - loss: 2.7608 - val_loss: 2.4337
Epoch 40/500
6/6 [==============================] - 2s 367ms/step - loss: 3.0540 - val_loss: 2.4107
Epoch 41/500
6/6 [==============================] - 2s 367ms/step - loss: 3.0056 - val_loss: 2.4283
Epoch 42/500
6/6 [==============================] - 2s 355ms/step - loss: 2.9555 - val_loss: 2.3939
Epoch 43/500
6/6 [==============================] - 2s 369ms/step - loss: 2.8453 - val_loss: 2.2705
Epoch 44/500
6/6 [==============================] - 2s 364ms/step - loss: 2.7015 - val_loss: 2.2502
Epoch 45/500
6/6 [==============================] - 2s 370ms/step - loss: 2.9481 - val_loss: 2.3176
Epoch 46/500
6/6 [==============================] - 2s 368ms/step - loss: 2.7662 - val_loss: 2.2379
Epoch 47/500
6/6 [==============================] - 2s 367ms/step - loss: 2.7584 - val_loss: 2.2080
Epoch 48/500
6/6 [==============================] - 2s 366ms/step - loss: 2.8197 - val_loss: 2.2317
Epoch 49/500
6/6 [==============================] - 2s 368ms/step - loss: 2.7954 - val_loss: 2.1908
Epoch 50/500
6/6 [==============================] - 2s 373ms/step - loss: 2.6722 - val_loss: 2.1719
Epoch 51/500
6/6 [==============================] - 2s 408ms/step - loss: 2.6723 - val_loss: 2.1741
Epoch 52/500
6/6 [==============================] - 2s 408ms/step - loss: 2.7345 - val_loss: 2.1729
Epoch 53/500
6/6 [==============================] - 2s 408ms/step - loss: 2.7182 - val_loss: 2.1159
Epoch 54/500
6/6 [==============================] - 2s 392ms/step - loss: 2.7483 - val_loss: 2.0885
Epoch 55/500
6/6 [==============================] - 2s 383ms/step - loss: 2.7622 - val_loss: 2.1012
Epoch 56/500
6/6 [==============================] - 2s 364ms/step - loss: 2.6531 - val_loss: 2.0887
Epoch 57/500
6/6 [==============================] - 2s 361ms/step - loss: 2.7732 - val_loss: 2.1424
Epoch 58/500
6/6 [==============================] - 2s 371ms/step - loss: 2.5085 - val_loss: 2.1334
Epoch 59/500
6/6 [==============================] - 2s 371ms/step - loss: 2.5888 - val_loss: 2.1150
Epoch 60/500
6/6 [==============================] - 2s 364ms/step - loss: 2.6892 - val_loss: 2.1991
Epoch 61/500
6/6 [==============================] - 2s 367ms/step - loss: 2.7403 - val_loss: 2.2561
Epoch 62/500
6/6 [==============================] - 2s 371ms/step - loss: 2.4785 - val_loss: 2.1881
Epoch 63/500
6/6 [==============================] - 2s 364ms/step - loss: 2.6917 - val_loss: 2.1823
Epoch 64/500
6/6 [==============================] - 2s 370ms/step - loss: 2.7629 - val_loss: 2.2101
Epoch 65/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5087 - val_loss: 2.1793
Epoch 66/500
6/6 [==============================] - 2s 371ms/step - loss: 2.8074 - val_loss: 2.1893
Epoch 67/500
6/6 [==============================] - 2s 370ms/step - loss: 2.5579 - val_loss: 2.2530
Epoch 68/500
6/6 [==============================] - 2s 364ms/step - loss: 2.7393 - val_loss: 2.2394
Epoch 69/500
6/6 [==============================] - 2s 364ms/step - loss: 2.5795 - val_loss: 2.1955
Epoch 70/500
6/6 [==============================] - 2s 367ms/step - loss: 2.8283 - val_loss: 2.1672
Epoch 71/500
6/6 [==============================] - 2s 361ms/step - loss: 2.7835 - val_loss: 2.1041
Epoch 72/500
6/6 [==============================] - 2s 411ms/step - loss: 2.7038 - val_loss: 2.0751
Epoch 73/500
6/6 [==============================] - 2s 374ms/step - loss: 2.6089 - val_loss: 2.0905
Epoch 74/500
6/6 [==============================] - 2s 367ms/step - loss: 2.6182 - val_loss: 2.0921
Epoch 75/500
6/6 [==============================] - 2s 370ms/step - loss: 2.5096 - val_loss: 2.0935
Epoch 76/500
6/6 [==============================] - 2s 371ms/step - loss: 2.4980 - val_loss: 2.1079
Epoch 77/500
6/6 [==============================] - 2s 364ms/step - loss: 2.4633 - val_loss: 2.1111
Epoch 78/500
6/6 [==============================] - 2s 358ms/step - loss: 2.5040 - val_loss: 2.0889
Epoch 79/500
6/6 [==============================] - 2s 402ms/step - loss: 2.4293 - val_loss: 2.0479
Epoch 80/500
6/6 [==============================] - 2s 405ms/step - loss: 2.5899 - val_loss: 2.0428
Epoch 81/500
6/6 [==============================] - 2s 374ms/step - loss: 2.7058 - val_loss: 2.0587
Epoch 82/500
6/6 [==============================] - 2s 402ms/step - loss: 2.4711 - val_loss: 2.0341
Epoch 83/500
6/6 [==============================] - 2s 364ms/step - loss: 2.5885 - val_loss: 2.0707
Epoch 84/500
6/6 [==============================] - 2s 367ms/step - loss: 2.7199 - val_loss: 2.0512
Epoch 85/500
6/6 [==============================] - 2s 364ms/step - loss: 2.5358 - val_loss: 2.0376
Epoch 86/500
6/6 [==============================] - 2s 402ms/step - loss: 2.4439 - val_loss: 2.0287
Epoch 87/500
6/6 [==============================] - 2s 377ms/step - loss: 2.5349 - val_loss: 2.0451
Epoch 88/500
6/6 [==============================] - 2s 374ms/step - loss: 2.4912 - val_loss: 2.1174
Epoch 89/500
6/6 [==============================] - 2s 373ms/step - loss: 2.6379 - val_loss: 2.0936
Epoch 90/500
6/6 [==============================] - 2s 373ms/step - loss: 2.5607 - val_loss: 2.1415
Epoch 91/500
6/6 [==============================] - 2s 368ms/step - loss: 2.5516 - val_loss: 2.1693
Epoch 92/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5934 - val_loss: 2.1330
Epoch 93/500
6/6 [==============================] - 2s 367ms/step - loss: 2.6772 - val_loss: 2.1931
Epoch 94/500
6/6 [==============================] - 2s 373ms/step - loss: 2.6642 - val_loss: 2.1111
Epoch 95/500
6/6 [==============================] - 2s 370ms/step - loss: 2.3582 - val_loss: 2.1107
Epoch 96/500
6/6 [==============================] - 2s 370ms/step - loss: 2.6490 - val_loss: 2.0883
Epoch 97/500
6/6 [==============================] - 2s 375ms/step - loss: 2.8540 - val_loss: 2.0518
Epoch 98/500
6/6 [==============================] - 2s 377ms/step - loss: 2.4089 - val_loss: 2.0622
Epoch 99/500
6/6 [==============================] - 2s 414ms/step - loss: 2.5415 - val_loss: 2.0232
Epoch 100/500
6/6 [==============================] - 2s 375ms/step - loss: 2.4719 - val_loss: 2.0396
Epoch 101/500
6/6 [==============================] - 2s 370ms/step - loss: 2.6304 - val_loss: 2.0410
Epoch 102/500
6/6 [==============================] - 2s 365ms/step - loss: 2.3662 - val_loss: 2.0379
Epoch 103/500
6/6 [==============================] - 2s 405ms/step - loss: 2.3166 - val_loss: 2.0134
Epoch 104/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4705 - val_loss: 1.9786
Epoch 105/500
6/6 [==============================] - 2s 409ms/step - loss: 2.2826 - val_loss: 1.9666
Epoch 106/500
6/6 [==============================] - 2s 368ms/step - loss: 2.5788 - val_loss: 2.0277
Epoch 107/500
6/6 [==============================] - 2s 383ms/step - loss: 2.5633 - val_loss: 2.0187
Epoch 108/500
6/6 [==============================] - 2s 361ms/step - loss: 2.4268 - val_loss: 1.9904
Epoch 109/500
6/6 [==============================] - 2s 405ms/step - loss: 2.3033 - val_loss: 1.9616
Epoch 110/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5514 - val_loss: 1.9577
Epoch 111/500
6/6 [==============================] - 2s 406ms/step - loss: 2.4815 - val_loss: 1.9299
Epoch 112/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5146 - val_loss: 1.9502
Epoch 113/500
6/6 [==============================] - 2s 361ms/step - loss: 2.4146 - val_loss: 1.9508
Epoch 114/500
6/6 [==============================] - 2s 374ms/step - loss: 2.4147 - val_loss: 1.9947
Epoch 115/500
6/6 [==============================] - 2s 371ms/step - loss: 2.5077 - val_loss: 2.0817
Epoch 116/500
6/6 [==============================] - 2s 368ms/step - loss: 2.4965 - val_loss: 2.0104
Epoch 117/500
6/6 [==============================] - 2s 365ms/step - loss: 2.5645 - val_loss: 1.9830
Epoch 118/500
6/6 [==============================] - 2s 368ms/step - loss: 2.4040 - val_loss: 2.0144
Epoch 119/500
6/6 [==============================] - 2s 371ms/step - loss: 2.5070 - val_loss: 1.9944
Epoch 120/500
6/6 [==============================] - 2s 365ms/step - loss: 2.4982 - val_loss: 1.9680
Epoch 121/500
6/6 [==============================] - 2s 371ms/step - loss: 2.5082 - val_loss: 1.9897
Epoch 122/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5069 - val_loss: 1.9941
Epoch 123/500
6/6 [==============================] - 2s 377ms/step - loss: 2.3454 - val_loss: 1.9861
Epoch 124/500
6/6 [==============================] - 2s 364ms/step - loss: 2.3309 - val_loss: 1.9946
Epoch 125/500
6/6 [==============================] - 2s 365ms/step - loss: 2.2993 - val_loss: 1.9886
Epoch 126/500
6/6 [==============================] - 2s 371ms/step - loss: 2.5021 - val_loss: 2.0339
Epoch 127/500
6/6 [==============================] - 2s 371ms/step - loss: 2.3743 - val_loss: 2.0969
Epoch 128/500
6/6 [==============================] - 2s 371ms/step - loss: 2.3800 - val_loss: 2.1154
Epoch 129/500
6/6 [==============================] - 2s 371ms/step - loss: 2.4772 - val_loss: 2.0313
Epoch 130/500
6/6 [==============================] - 2s 358ms/step - loss: 2.6487 - val_loss: 2.0617
Epoch 131/500
6/6 [==============================] - 3s 512ms/step - loss: 2.2817 - val_loss: 2.0895
Epoch 131: early stopping
Epoch 1/500
6/6 [==============================] - 36s 801ms/step - loss: 3.6069 - val_loss: 2.1072
Epoch 2/500
6/6 [==============================] - 3s 411ms/step - loss: 3.0487 - val_loss: 2.3930
Epoch 3/500
6/6 [==============================] - 3s 421ms/step - loss: 2.7310 - val_loss: 2.5475
Epoch 4/500
6/6 [==============================] - 3s 426ms/step - loss: 2.5135 - val_loss: 2.6042
Epoch 5/500
6/6 [==============================] - 3s 427ms/step - loss: 2.4468 - val_loss: 2.6928
Epoch 6/500
6/6 [==============================] - 3s 444ms/step - loss: 2.3255 - val_loss: 2.4663
Epoch 7/500
6/6 [==============================] - 3s 431ms/step - loss: 2.2249 - val_loss: 2.3431
Epoch 8/500
6/6 [==============================] - 3s 474ms/step - loss: 2.0516 - val_loss: 2.2800
Epoch 9/500
6/6 [==============================] - 3s 430ms/step - loss: 1.9894 - val_loss: 2.2948
Epoch 10/500
6/6 [==============================] - 3s 454ms/step - loss: 2.0767 - val_loss: 2.2373
Epoch 11/500
6/6 [==============================] - 3s 454ms/step - loss: 1.8376 - val_loss: 2.2145
Epoch 12/500
6/6 [==============================] - 3s 453ms/step - loss: 1.8462 - val_loss: 2.2003
Epoch 13/500
6/6 [==============================] - 3s 483ms/step - loss: 1.9277 - val_loss: 2.0868
Epoch 14/500
6/6 [==============================] - 3s 473ms/step - loss: 1.7504 - val_loss: 2.0846
Epoch 15/500
6/6 [==============================] - 3s 468ms/step - loss: 1.7165 - val_loss: 2.0845
Epoch 16/500
6/6 [==============================] - 3s 484ms/step - loss: 1.9754 - val_loss: 2.0478
Epoch 17/500
6/6 [==============================] - 3s 468ms/step - loss: 1.7558 - val_loss: 2.0295
Epoch 18/500
6/6 [==============================] - 3s 461ms/step - loss: 1.6866 - val_loss: 1.9123
Epoch 19/500
6/6 [==============================] - 3s 430ms/step - loss: 1.5674 - val_loss: 1.9230
Epoch 20/500
6/6 [==============================] - 3s 482ms/step - loss: 1.6200 - val_loss: 1.8804
Epoch 21/500
6/6 [==============================] - 3s 474ms/step - loss: 1.6090 - val_loss: 1.8397
Epoch 22/500
6/6 [==============================] - 3s 456ms/step - loss: 1.6776 - val_loss: 1.8356
Epoch 23/500
6/6 [==============================] - 3s 452ms/step - loss: 1.6117 - val_loss: 1.8006
Epoch 24/500
6/6 [==============================] - 3s 439ms/step - loss: 1.6885 - val_loss: 1.8123
Epoch 25/500
6/6 [==============================] - 3s 499ms/step - loss: 1.7033 - val_loss: 1.7549
Epoch 26/500
6/6 [==============================] - 3s 436ms/step - loss: 1.6045 - val_loss: 1.7588
Epoch 27/500
6/6 [==============================] - 3s 446ms/step - loss: 1.5857 - val_loss: 1.8462
Epoch 28/500
6/6 [==============================] - 3s 486ms/step - loss: 1.7330 - val_loss: 1.7294
Epoch 29/500
6/6 [==============================] - 3s 464ms/step - loss: 1.6750 - val_loss: 1.6500
Epoch 30/500
6/6 [==============================] - 3s 454ms/step - loss: 1.5293 - val_loss: 1.5841
Epoch 31/500
6/6 [==============================] - 3s 465ms/step - loss: 1.8102 - val_loss: 1.5584
Epoch 32/500
6/6 [==============================] - 3s 453ms/step - loss: 1.6208 - val_loss: 1.5552
Epoch 33/500
6/6 [==============================] - 3s 424ms/step - loss: 1.4615 - val_loss: 1.5781
Epoch 34/500
6/6 [==============================] - 3s 462ms/step - loss: 1.5135 - val_loss: 1.5998
Epoch 35/500
6/6 [==============================] - 3s 440ms/step - loss: 1.5354 - val_loss: 1.5893
Epoch 36/500
6/6 [==============================] - 3s 454ms/step - loss: 1.4814 - val_loss: 1.5768
Epoch 37/500
6/6 [==============================] - 3s 464ms/step - loss: 1.5468 - val_loss: 1.5567
Epoch 38/500
6/6 [==============================] - 3s 480ms/step - loss: 1.6241 - val_loss: 1.5371
Epoch 39/500
6/6 [==============================] - 3s 478ms/step - loss: 1.5167 - val_loss: 1.5343
Epoch 40/500
6/6 [==============================] - 3s 493ms/step - loss: 1.5166 - val_loss: 1.5150
Epoch 41/500
6/6 [==============================] - 3s 471ms/step - loss: 1.3638 - val_loss: 1.4870
Epoch 42/500
6/6 [==============================] - 3s 460ms/step - loss: 1.4416 - val_loss: 1.4783
Epoch 43/500
6/6 [==============================] - 3s 483ms/step - loss: 1.4266 - val_loss: 1.4735
Epoch 44/500
6/6 [==============================] - 3s 436ms/step - loss: 1.5208 - val_loss: 1.5049
Epoch 45/500
6/6 [==============================] - 3s 431ms/step - loss: 1.3676 - val_loss: 1.5023
Epoch 46/500
6/6 [==============================] - 3s 444ms/step - loss: 1.4167 - val_loss: 1.4773
Epoch 47/500
6/6 [==============================] - 3s 456ms/step - loss: 1.4951 - val_loss: 1.4986
Epoch 48/500
6/6 [==============================] - 3s 455ms/step - loss: 1.4392 - val_loss: 1.5692
Epoch 49/500
6/6 [==============================] - 3s 437ms/step - loss: 1.4630 - val_loss: 1.5351
Epoch 50/500
6/6 [==============================] - 3s 459ms/step - loss: 1.4748 - val_loss: 1.5700
Epoch 51/500
6/6 [==============================] - 3s 456ms/step - loss: 1.3698 - val_loss: 1.5128
Epoch 52/500
6/6 [==============================] - 3s 463ms/step - loss: 1.4200 - val_loss: 1.4744
Epoch 53/500
6/6 [==============================] - 3s 462ms/step - loss: 1.3819 - val_loss: 1.5175
Epoch 54/500
6/6 [==============================] - 3s 456ms/step - loss: 1.3731 - val_loss: 1.4869
Epoch 55/500
6/6 [==============================] - 3s 448ms/step - loss: 1.3789 - val_loss: 1.5080
Epoch 56/500
6/6 [==============================] - 3s 455ms/step - loss: 1.3574 - val_loss: 1.4769
Epoch 57/500
6/6 [==============================] - 3s 460ms/step - loss: 1.4273 - val_loss: 1.4860
Epoch 58/500
6/6 [==============================] - 3s 460ms/step - loss: 1.4877 - val_loss: 1.5188
Epoch 59/500
6/6 [==============================] - 3s 460ms/step - loss: 1.4384 - val_loss: 1.5131
Epoch 60/500
6/6 [==============================] - 3s 466ms/step - loss: 1.5801 - val_loss: 1.4881
Epoch 61/500
6/6 [==============================] - 3s 493ms/step - loss: 1.3952 - val_loss: 1.4604
Epoch 62/500
6/6 [==============================] - 3s 498ms/step - loss: 1.4102 - val_loss: 1.4355
Epoch 63/500
6/6 [==============================] - 3s 465ms/step - loss: 1.4331 - val_loss: 1.4624
Epoch 64/500
6/6 [==============================] - 3s 490ms/step - loss: 1.3324 - val_loss: 1.4165
Epoch 65/500
6/6 [==============================] - 3s 467ms/step - loss: 1.3952 - val_loss: 1.4178
Epoch 66/500
6/6 [==============================] - 3s 468ms/step - loss: 1.4652 - val_loss: 1.4369
Epoch 67/500
6/6 [==============================] - 3s 465ms/step - loss: 1.3772 - val_loss: 1.5402
Epoch 68/500
6/6 [==============================] - 3s 465ms/step - loss: 1.3057 - val_loss: 1.6232
Epoch 69/500
6/6 [==============================] - 3s 461ms/step - loss: 1.4052 - val_loss: 1.5508
Epoch 70/500
6/6 [==============================] - 3s 453ms/step - loss: 1.4022 - val_loss: 1.5086
Epoch 71/500
6/6 [==============================] - 3s 460ms/step - loss: 1.3559 - val_loss: 1.4682
Epoch 72/500
6/6 [==============================] - 3s 467ms/step - loss: 1.2874 - val_loss: 1.4791
Epoch 73/500
6/6 [==============================] - 3s 462ms/step - loss: 1.3008 - val_loss: 1.4831
Epoch 74/500
6/6 [==============================] - 3s 472ms/step - loss: 1.1971 - val_loss: 1.5114
Epoch 75/500
6/6 [==============================] - 3s 456ms/step - loss: 1.4104 - val_loss: 1.5211
Epoch 76/500
6/6 [==============================] - 3s 449ms/step - loss: 1.2348 - val_loss: 1.4553
Epoch 77/500
6/6 [==============================] - 3s 470ms/step - loss: 1.3623 - val_loss: 1.4464
Epoch 78/500
6/6 [==============================] - 3s 496ms/step - loss: 1.3730 - val_loss: 1.4130
Epoch 79/500
6/6 [==============================] - 3s 455ms/step - loss: 1.3453 - val_loss: 1.4283
Epoch 80/500
6/6 [==============================] - 3s 466ms/step - loss: 1.3770 - val_loss: 1.4277
Epoch 81/500
6/6 [==============================] - 3s 497ms/step - loss: 1.3444 - val_loss: 1.3952
Epoch 82/500
6/6 [==============================] - 3s 460ms/step - loss: 1.4049 - val_loss: 1.4336
Epoch 83/500
6/6 [==============================] - 3s 457ms/step - loss: 1.3585 - val_loss: 1.4579
Epoch 84/500
6/6 [==============================] - 3s 457ms/step - loss: 1.4691 - val_loss: 1.4852
Epoch 85/500
6/6 [==============================] - 3s 459ms/step - loss: 1.2757 - val_loss: 1.5147
Epoch 86/500
6/6 [==============================] - 3s 462ms/step - loss: 1.2368 - val_loss: 1.4801
Epoch 87/500
6/6 [==============================] - 3s 457ms/step - loss: 1.2692 - val_loss: 1.5451
Epoch 88/500
6/6 [==============================] - 3s 468ms/step - loss: 1.3676 - val_loss: 1.5013
Epoch 89/500
6/6 [==============================] - 3s 457ms/step - loss: 1.2994 - val_loss: 1.5113
Epoch 90/500
6/6 [==============================] - 3s 447ms/step - loss: 1.1827 - val_loss: 1.4112
Epoch 91/500
6/6 [==============================] - 3s 469ms/step - loss: 1.2131 - val_loss: 1.5089
Epoch 92/500
6/6 [==============================] - 3s 457ms/step - loss: 1.3270 - val_loss: 1.4241
Epoch 93/500
6/6 [==============================] - 3s 457ms/step - loss: 1.2941 - val_loss: 1.4339
Epoch 94/500
6/6 [==============================] - 3s 468ms/step - loss: 1.2930 - val_loss: 1.4483
Epoch 95/500
6/6 [==============================] - 3s 458ms/step - loss: 1.2306 - val_loss: 1.4613
Epoch 96/500
6/6 [==============================] - 3s 457ms/step - loss: 1.1499 - val_loss: 1.4884
Epoch 97/500
6/6 [==============================] - 3s 457ms/step - loss: 1.3384 - val_loss: 1.4588
Epoch 98/500
6/6 [==============================] - 3s 465ms/step - loss: 1.1576 - val_loss: 1.4412
Epoch 99/500
6/6 [==============================] - 3s 450ms/step - loss: 1.2096 - val_loss: 1.4000
Epoch 100/500
6/6 [==============================] - 3s 465ms/step - loss: 1.1570 - val_loss: 1.4194
Epoch 101/500
6/6 [==============================] - 3s 501ms/step - loss: 1.2342 - val_loss: 1.3910
Epoch 102/500
6/6 [==============================] - 3s 503ms/step - loss: 1.1822 - val_loss: 1.3554
Epoch 103/500
6/6 [==============================] - 3s 497ms/step - loss: 1.2455 - val_loss: 1.3368
Epoch 104/500
6/6 [==============================] - 3s 453ms/step - loss: 1.2776 - val_loss: 1.3575
Epoch 105/500
6/6 [==============================] - 3s 451ms/step - loss: 1.2646 - val_loss: 1.3455
Epoch 106/500
6/6 [==============================] - 3s 456ms/step - loss: 1.2634 - val_loss: 1.3911
Epoch 107/500
6/6 [==============================] - 3s 463ms/step - loss: 1.2077 - val_loss: 1.3945
Epoch 108/500
6/6 [==============================] - 3s 460ms/step - loss: 1.2115 - val_loss: 1.4395
Epoch 109/500
6/6 [==============================] - 3s 455ms/step - loss: 1.1689 - val_loss: 1.4656
Epoch 110/500
6/6 [==============================] - 3s 470ms/step - loss: 1.2478 - val_loss: 1.4145
Epoch 111/500
6/6 [==============================] - 3s 459ms/step - loss: 1.1801 - val_loss: 1.3553
Epoch 112/500
6/6 [==============================] - 3s 461ms/step - loss: 1.1876 - val_loss: 1.3697
Epoch 113/500
6/6 [==============================] - 3s 461ms/step - loss: 1.1553 - val_loss: 1.3861
Epoch 114/500
6/6 [==============================] - 3s 465ms/step - loss: 1.2248 - val_loss: 1.4092
Epoch 115/500
6/6 [==============================] - 3s 460ms/step - loss: 1.1728 - val_loss: 1.3632
Epoch 116/500
6/6 [==============================] - 3s 454ms/step - loss: 1.2966 - val_loss: 1.3946
Epoch 117/500
6/6 [==============================] - 3s 464ms/step - loss: 1.1772 - val_loss: 1.3776
Epoch 118/500
6/6 [==============================] - 3s 451ms/step - loss: 1.2486 - val_loss: 1.3769
Epoch 119/500
6/6 [==============================] - 3s 457ms/step - loss: 1.3127 - val_loss: 1.3747
Epoch 120/500
6/6 [==============================] - 3s 468ms/step - loss: 1.2533 - val_loss: 1.4026
Epoch 121/500
6/6 [==============================] - 3s 462ms/step - loss: 1.2819 - val_loss: 1.3801
Epoch 122/500
6/6 [==============================] - 3s 461ms/step - loss: 1.2364 - val_loss: 1.3821
Epoch 123/500
6/6 [==============================] - 4s 755ms/step - loss: 1.1686 - val_loss: 1.4021
Epoch 123: early stopping
Epoch 1/500
6/6 [==============================] - 11s 698ms/step - loss: 9.8236 - val_loss: 9.1203
Epoch 2/500
6/6 [==============================] - 2s 380ms/step - loss: 8.2116 - val_loss: 8.2788
Epoch 3/500
6/6 [==============================] - 2s 380ms/step - loss: 7.2402 - val_loss: 8.1813
Epoch 4/500
6/6 [==============================] - 2s 374ms/step - loss: 6.1797 - val_loss: 5.5584
Epoch 5/500
6/6 [==============================] - 2s 380ms/step - loss: 5.5968 - val_loss: 4.9156
Epoch 6/500
6/6 [==============================] - 2s 380ms/step - loss: 5.2907 - val_loss: 5.4151
Epoch 7/500
6/6 [==============================] - 2s 374ms/step - loss: 5.3332 - val_loss: 6.1493
Epoch 8/500
6/6 [==============================] - 2s 382ms/step - loss: 4.4196 - val_loss: 4.6050
Epoch 9/500
6/6 [==============================] - 2s 390ms/step - loss: 4.3892 - val_loss: 4.4460
Epoch 10/500
6/6 [==============================] - 2s 380ms/step - loss: 4.4216 - val_loss: 4.6595
Epoch 11/500
6/6 [==============================] - 2s 377ms/step - loss: 4.4048 - val_loss: 4.2866
Epoch 12/500
6/6 [==============================] - 2s 387ms/step - loss: 3.9037 - val_loss: 5.1193
Epoch 13/500
6/6 [==============================] - 2s 384ms/step - loss: 4.0525 - val_loss: 4.9708
Epoch 14/500
6/6 [==============================] - 2s 380ms/step - loss: 3.8588 - val_loss: 4.3063
Epoch 15/500
6/6 [==============================] - 2s 387ms/step - loss: 3.7062 - val_loss: 3.7423
Epoch 16/500
6/6 [==============================] - 2s 377ms/step - loss: 3.4111 - val_loss: 3.8201
Epoch 17/500
6/6 [==============================] - 2s 377ms/step - loss: 3.5986 - val_loss: 3.5031
Epoch 18/500
6/6 [==============================] - 2s 380ms/step - loss: 3.3851 - val_loss: 3.2811
Epoch 19/500
6/6 [==============================] - 2s 374ms/step - loss: 3.3554 - val_loss: 2.8679
Epoch 20/500
6/6 [==============================] - 2s 380ms/step - loss: 3.4722 - val_loss: 2.9463
Epoch 21/500
6/6 [==============================] - 2s 380ms/step - loss: 3.4034 - val_loss: 3.0351
Epoch 22/500
6/6 [==============================] - 2s 377ms/step - loss: 3.3546 - val_loss: 2.9311
Epoch 23/500
6/6 [==============================] - 2s 383ms/step - loss: 3.4728 - val_loss: 2.8053
Epoch 24/500
6/6 [==============================] - 2s 383ms/step - loss: 3.1939 - val_loss: 2.6891
Epoch 25/500
6/6 [==============================] - 2s 384ms/step - loss: 3.0841 - val_loss: 2.4800
Epoch 26/500
6/6 [==============================] - 2s 385ms/step - loss: 3.0460 - val_loss: 2.5013
Epoch 27/500
6/6 [==============================] - 2s 382ms/step - loss: 2.9963 - val_loss: 2.3983
Epoch 28/500
6/6 [==============================] - 2s 377ms/step - loss: 3.1822 - val_loss: 2.3510
Epoch 29/500
6/6 [==============================] - 2s 381ms/step - loss: 3.0598 - val_loss: 2.2832
Epoch 30/500
6/6 [==============================] - 2s 383ms/step - loss: 3.0346 - val_loss: 2.3013
Epoch 31/500
6/6 [==============================] - 2s 384ms/step - loss: 2.9938 - val_loss: 2.2574
Epoch 32/500
6/6 [==============================] - 2s 371ms/step - loss: 3.1263 - val_loss: 2.3365
Epoch 33/500
6/6 [==============================] - 2s 380ms/step - loss: 2.8845 - val_loss: 2.2860
Epoch 34/500
6/6 [==============================] - 2s 380ms/step - loss: 3.1754 - val_loss: 2.3702
Epoch 35/500
6/6 [==============================] - 2s 390ms/step - loss: 3.0402 - val_loss: 2.2276
Epoch 36/500
6/6 [==============================] - 2s 377ms/step - loss: 2.8304 - val_loss: 2.1704
Epoch 37/500
6/6 [==============================] - 2s 380ms/step - loss: 3.0273 - val_loss: 2.1956
Epoch 38/500
6/6 [==============================] - 2s 364ms/step - loss: 3.0339 - val_loss: 2.2174
Epoch 39/500
6/6 [==============================] - 2s 386ms/step - loss: 2.7363 - val_loss: 2.2294
Epoch 40/500
6/6 [==============================] - 2s 377ms/step - loss: 2.9108 - val_loss: 2.3108
Epoch 41/500
6/6 [==============================] - 2s 377ms/step - loss: 3.0877 - val_loss: 2.2711
Epoch 42/500
6/6 [==============================] - 2s 387ms/step - loss: 2.8825 - val_loss: 2.2751
Epoch 43/500
6/6 [==============================] - 2s 371ms/step - loss: 2.9577 - val_loss: 2.2806
Epoch 44/500
6/6 [==============================] - 2s 371ms/step - loss: 2.8745 - val_loss: 2.2726
Epoch 45/500
6/6 [==============================] - 2s 377ms/step - loss: 2.9198 - val_loss: 2.2895
Epoch 46/500
6/6 [==============================] - 2s 384ms/step - loss: 2.8514 - val_loss: 2.3379
Epoch 47/500
6/6 [==============================] - 2s 385ms/step - loss: 2.8335 - val_loss: 2.2529
Epoch 48/500
6/6 [==============================] - 2s 374ms/step - loss: 2.6435 - val_loss: 2.2282
Epoch 49/500
6/6 [==============================] - 2s 380ms/step - loss: 2.7776 - val_loss: 2.2035
Epoch 50/500
6/6 [==============================] - 2s 380ms/step - loss: 2.8930 - val_loss: 2.1742
Epoch 51/500
6/6 [==============================] - 3s 434ms/step - loss: 2.8285 - val_loss: 2.2169
Epoch 52/500
6/6 [==============================] - 2s 371ms/step - loss: 3.0343 - val_loss: 2.2435
Epoch 53/500
6/6 [==============================] - 2s 377ms/step - loss: 2.9776 - val_loss: 2.2348
Epoch 54/500
6/6 [==============================] - 3s 418ms/step - loss: 2.6366 - val_loss: 2.2087
Epoch 55/500
6/6 [==============================] - 2s 387ms/step - loss: 2.8551 - val_loss: 2.2840
Epoch 56/500
6/6 [==============================] - 2s 377ms/step - loss: 2.7462 - val_loss: 2.2500
Epoch 57/500
6/6 [==============================] - 2s 374ms/step - loss: 2.7676 - val_loss: 2.2989
Epoch 58/500
6/6 [==============================] - 3s 424ms/step - loss: 2.8343 - val_loss: 2.1734
Epoch 59/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6423 - val_loss: 2.2428
Epoch 60/500
6/6 [==============================] - 2s 371ms/step - loss: 2.7912 - val_loss: 2.1895
Epoch 61/500
6/6 [==============================] - 3s 434ms/step - loss: 2.5197 - val_loss: 2.1660
Epoch 62/500
6/6 [==============================] - 2s 383ms/step - loss: 2.6998 - val_loss: 2.1686
Epoch 63/500
6/6 [==============================] - 2s 384ms/step - loss: 2.4123 - val_loss: 2.1784
Epoch 64/500
6/6 [==============================] - 3s 427ms/step - loss: 2.6038 - val_loss: 2.1456
Epoch 65/500
6/6 [==============================] - 3s 421ms/step - loss: 2.6300 - val_loss: 2.0957
Epoch 66/500
6/6 [==============================] - 2s 384ms/step - loss: 2.8072 - val_loss: 2.1161
Epoch 67/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5892 - val_loss: 2.1529
Epoch 68/500
6/6 [==============================] - 2s 387ms/step - loss: 2.7823 - val_loss: 2.1626
Epoch 69/500
6/6 [==============================] - 2s 371ms/step - loss: 2.7407 - val_loss: 2.1660
Epoch 70/500
6/6 [==============================] - 2s 385ms/step - loss: 2.7489 - val_loss: 2.0974
Epoch 71/500
6/6 [==============================] - 3s 421ms/step - loss: 2.7517 - val_loss: 2.0751
Epoch 72/500
6/6 [==============================] - 3s 424ms/step - loss: 2.6804 - val_loss: 2.0741
Epoch 73/500
6/6 [==============================] - 3s 418ms/step - loss: 2.5436 - val_loss: 2.0218
Epoch 74/500
6/6 [==============================] - 2s 418ms/step - loss: 2.8561 - val_loss: 1.9940
Epoch 75/500
6/6 [==============================] - 2s 381ms/step - loss: 2.8194 - val_loss: 2.0274
Epoch 76/500
6/6 [==============================] - 2s 380ms/step - loss: 2.5841 - val_loss: 2.0081
Epoch 77/500
6/6 [==============================] - 2s 383ms/step - loss: 2.5097 - val_loss: 2.0357
Epoch 78/500
6/6 [==============================] - 2s 386ms/step - loss: 2.7430 - val_loss: 2.0539
Epoch 79/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5274 - val_loss: 2.0848
Epoch 80/500
6/6 [==============================] - 2s 370ms/step - loss: 2.6141 - val_loss: 2.0123
Epoch 81/500
6/6 [==============================] - 2s 380ms/step - loss: 2.4600 - val_loss: 2.0402
Epoch 82/500
6/6 [==============================] - 2s 386ms/step - loss: 2.7011 - val_loss: 2.0182
Epoch 83/500
6/6 [==============================] - 2s 377ms/step - loss: 2.5735 - val_loss: 2.0853
Epoch 84/500
6/6 [==============================] - 2s 373ms/step - loss: 2.6285 - val_loss: 2.0459
Epoch 85/500
6/6 [==============================] - 2s 382ms/step - loss: 2.5882 - val_loss: 2.0360
Epoch 86/500
6/6 [==============================] - 2s 392ms/step - loss: 2.5272 - val_loss: 2.0721
Epoch 87/500
6/6 [==============================] - 2s 378ms/step - loss: 2.5352 - val_loss: 2.0696
Epoch 88/500
6/6 [==============================] - 2s 373ms/step - loss: 2.4571 - val_loss: 2.0848
Epoch 89/500
6/6 [==============================] - 2s 380ms/step - loss: 2.3860 - val_loss: 2.0375
Epoch 90/500
6/6 [==============================] - 2s 373ms/step - loss: 2.5974 - val_loss: 2.0489
Epoch 91/500
6/6 [==============================] - 2s 383ms/step - loss: 2.4823 - val_loss: 2.0568
Epoch 92/500
6/6 [==============================] - 2s 382ms/step - loss: 2.2951 - val_loss: 2.0056
Epoch 93/500
6/6 [==============================] - 2s 370ms/step - loss: 2.4658 - val_loss: 2.0318
Epoch 94/500
6/6 [==============================] - 3s 534ms/step - loss: 2.4538 - val_loss: 2.0739
Epoch 94: early stopping
Epoch 1/500
6/6 [==============================] - 36s 876ms/step - loss: 4.0206 - val_loss: 2.0869
Epoch 2/500
6/6 [==============================] - 3s 430ms/step - loss: 3.1170 - val_loss: 2.4861
Epoch 3/500
6/6 [==============================] - 3s 428ms/step - loss: 2.6445 - val_loss: 2.6576
Epoch 4/500
6/6 [==============================] - 3s 414ms/step - loss: 2.5896 - val_loss: 2.7781
Epoch 5/500
6/6 [==============================] - 3s 445ms/step - loss: 2.4668 - val_loss: 2.7457
Epoch 6/500
6/6 [==============================] - 3s 464ms/step - loss: 2.4205 - val_loss: 2.7446
Epoch 7/500
6/6 [==============================] - 3s 469ms/step - loss: 2.1370 - val_loss: 2.4383
Epoch 8/500
6/6 [==============================] - 3s 477ms/step - loss: 2.2538 - val_loss: 2.3379
Epoch 9/500
6/6 [==============================] - 3s 462ms/step - loss: 2.1564 - val_loss: 2.2885
Epoch 10/500
6/6 [==============================] - 3s 486ms/step - loss: 2.0654 - val_loss: 2.1944
Epoch 11/500
6/6 [==============================] - 3s 474ms/step - loss: 2.0329 - val_loss: 2.0928
Epoch 12/500
6/6 [==============================] - 3s 463ms/step - loss: 2.1403 - val_loss: 2.0077
Epoch 13/500
6/6 [==============================] - 3s 431ms/step - loss: 1.9667 - val_loss: 2.1432
Epoch 14/500
6/6 [==============================] - 3s 450ms/step - loss: 1.9592 - val_loss: 2.1758
Epoch 15/500
6/6 [==============================] - 3s 450ms/step - loss: 1.8832 - val_loss: 2.0917
Epoch 16/500
6/6 [==============================] - 3s 465ms/step - loss: 1.9331 - val_loss: 2.0106
Epoch 17/500
6/6 [==============================] - 3s 437ms/step - loss: 1.8726 - val_loss: 2.0285
Epoch 18/500
6/6 [==============================] - 3s 510ms/step - loss: 1.8216 - val_loss: 1.9472
Epoch 19/500
6/6 [==============================] - 3s 489ms/step - loss: 1.7417 - val_loss: 1.8912
Epoch 20/500
6/6 [==============================] - 3s 473ms/step - loss: 1.7783 - val_loss: 1.8453
Epoch 21/500
6/6 [==============================] - 3s 483ms/step - loss: 1.6885 - val_loss: 1.8441
Epoch 22/500
6/6 [==============================] - 3s 486ms/step - loss: 1.6451 - val_loss: 1.7647
Epoch 23/500
6/6 [==============================] - 3s 477ms/step - loss: 1.8150 - val_loss: 1.7466
Epoch 24/500
6/6 [==============================] - 3s 436ms/step - loss: 1.7858 - val_loss: 1.7574
Epoch 25/500
6/6 [==============================] - 3s 469ms/step - loss: 1.6704 - val_loss: 1.7553
Epoch 26/500
6/6 [==============================] - 3s 451ms/step - loss: 1.6435 - val_loss: 1.7963
Epoch 27/500
6/6 [==============================] - 3s 491ms/step - loss: 1.8016 - val_loss: 1.8575
Epoch 28/500
6/6 [==============================] - 3s 459ms/step - loss: 1.6342 - val_loss: 1.7756
Epoch 29/500
6/6 [==============================] - 3s 525ms/step - loss: 1.7903 - val_loss: 1.7342
Epoch 30/500
6/6 [==============================] - 3s 449ms/step - loss: 1.6049 - val_loss: 1.7478
Epoch 31/500
6/6 [==============================] - 3s 504ms/step - loss: 1.7084 - val_loss: 1.7001
Epoch 32/500
6/6 [==============================] - 3s 505ms/step - loss: 1.6149 - val_loss: 1.6231
Epoch 33/500
6/6 [==============================] - 3s 435ms/step - loss: 1.5839 - val_loss: 1.6386
Epoch 34/500
6/6 [==============================] - 3s 496ms/step - loss: 1.5444 - val_loss: 1.6227
Epoch 35/500
6/6 [==============================] - 3s 461ms/step - loss: 1.5827 - val_loss: 1.6386
Epoch 36/500
6/6 [==============================] - 3s 458ms/step - loss: 1.5623 - val_loss: 1.6374
Epoch 37/500
6/6 [==============================] - 3s 498ms/step - loss: 1.5996 - val_loss: 1.6050
Epoch 38/500
6/6 [==============================] - 3s 472ms/step - loss: 1.5367 - val_loss: 1.6801
Epoch 39/500
6/6 [==============================] - 3s 470ms/step - loss: 1.5847 - val_loss: 1.6334
Epoch 40/500
6/6 [==============================] - 3s 511ms/step - loss: 1.6777 - val_loss: 1.5870
Epoch 41/500
6/6 [==============================] - 3s 509ms/step - loss: 1.4876 - val_loss: 1.5447
Epoch 42/500
6/6 [==============================] - 3s 506ms/step - loss: 1.6118 - val_loss: 1.5266
Epoch 43/500
6/6 [==============================] - 3s 498ms/step - loss: 1.5321 - val_loss: 1.5207
Epoch 44/500
6/6 [==============================] - 3s 464ms/step - loss: 1.5639 - val_loss: 1.5248
Epoch 45/500
6/6 [==============================] - 3s 491ms/step - loss: 1.6891 - val_loss: 1.4985
Epoch 46/500
6/6 [==============================] - 3s 461ms/step - loss: 1.7028 - val_loss: 1.5406
Epoch 47/500
6/6 [==============================] - 3s 471ms/step - loss: 1.5360 - val_loss: 1.5486
Epoch 48/500
6/6 [==============================] - 3s 469ms/step - loss: 1.5203 - val_loss: 1.5704
Epoch 49/500
6/6 [==============================] - 3s 465ms/step - loss: 1.4389 - val_loss: 1.5393
Epoch 50/500
6/6 [==============================] - 3s 466ms/step - loss: 1.6065 - val_loss: 1.5418
Epoch 51/500
6/6 [==============================] - 3s 460ms/step - loss: 1.5027 - val_loss: 1.5309
Epoch 52/500
6/6 [==============================] - 3s 517ms/step - loss: 1.4616 - val_loss: 1.4740
Epoch 53/500
6/6 [==============================] - 3s 504ms/step - loss: 1.4874 - val_loss: 1.4657
Epoch 54/500
6/6 [==============================] - 3s 468ms/step - loss: 1.4831 - val_loss: 1.4892
Epoch 55/500
6/6 [==============================] - 3s 495ms/step - loss: 1.4376 - val_loss: 1.4557
Epoch 56/500
6/6 [==============================] - 3s 500ms/step - loss: 1.3497 - val_loss: 1.4353
Epoch 57/500
6/6 [==============================] - 3s 463ms/step - loss: 1.5916 - val_loss: 1.4415
Epoch 58/500
6/6 [==============================] - 3s 476ms/step - loss: 1.4665 - val_loss: 1.4742
Epoch 59/500
6/6 [==============================] - 3s 449ms/step - loss: 1.4516 - val_loss: 1.4808
Epoch 60/500
6/6 [==============================] - 3s 466ms/step - loss: 1.5718 - val_loss: 1.4949
Epoch 61/500
6/6 [==============================] - 3s 493ms/step - loss: 1.3393 - val_loss: 1.4746
Epoch 62/500
6/6 [==============================] - 3s 473ms/step - loss: 1.4182 - val_loss: 1.4576
Epoch 63/500
6/6 [==============================] - 3s 478ms/step - loss: 1.4023 - val_loss: 1.4481
Epoch 64/500
6/6 [==============================] - 3s 468ms/step - loss: 1.4604 - val_loss: 1.4584
Epoch 65/500
6/6 [==============================] - 3s 460ms/step - loss: 1.5534 - val_loss: 1.4595
Epoch 66/500
6/6 [==============================] - 3s 465ms/step - loss: 1.4927 - val_loss: 1.4470
Epoch 67/500
6/6 [==============================] - 3s 498ms/step - loss: 1.4048 - val_loss: 1.4349
Epoch 68/500
6/6 [==============================] - 3s 474ms/step - loss: 1.4814 - val_loss: 1.4777
Epoch 69/500
6/6 [==============================] - 3s 473ms/step - loss: 1.4369 - val_loss: 1.5226
Epoch 70/500
6/6 [==============================] - 3s 484ms/step - loss: 1.3828 - val_loss: 1.5066
Epoch 71/500
6/6 [==============================] - 3s 481ms/step - loss: 1.4303 - val_loss: 1.5026
Epoch 72/500
6/6 [==============================] - 3s 481ms/step - loss: 1.4808 - val_loss: 1.4997
Epoch 73/500
6/6 [==============================] - 3s 483ms/step - loss: 1.3793 - val_loss: 1.5731
Epoch 74/500
6/6 [==============================] - 3s 491ms/step - loss: 1.3467 - val_loss: 1.5909
Epoch 75/500
6/6 [==============================] - 3s 483ms/step - loss: 1.3756 - val_loss: 1.5933
Epoch 76/500
6/6 [==============================] - 3s 487ms/step - loss: 1.4239 - val_loss: 1.5820
Epoch 77/500
6/6 [==============================] - 3s 489ms/step - loss: 1.5407 - val_loss: 1.5965
Epoch 78/500
6/6 [==============================] - 3s 475ms/step - loss: 1.4362 - val_loss: 1.5630
Epoch 79/500
6/6 [==============================] - 3s 495ms/step - loss: 1.5705 - val_loss: 1.5151
Epoch 80/500
6/6 [==============================] - 3s 474ms/step - loss: 1.5334 - val_loss: 1.4818
Epoch 81/500
6/6 [==============================] - 3s 485ms/step - loss: 1.4247 - val_loss: 1.5095
Epoch 82/500
6/6 [==============================] - 3s 496ms/step - loss: 1.3823 - val_loss: 1.4883
Epoch 83/500
6/6 [==============================] - 3s 482ms/step - loss: 1.5162 - val_loss: 1.5137
Epoch 84/500
6/6 [==============================] - 3s 465ms/step - loss: 1.3204 - val_loss: 1.4946
Epoch 85/500
6/6 [==============================] - 3s 493ms/step - loss: 1.3262 - val_loss: 1.5020
Epoch 86/500
6/6 [==============================] - 3s 493ms/step - loss: 1.2867 - val_loss: 1.5157
Epoch 87/500
6/6 [==============================] - 5s 777ms/step - loss: 1.3174 - val_loss: 1.5274
Epoch 87: early stopping
Epoch 1/500
6/6 [==============================] - 10s 694ms/step - loss: 9.7790 - val_loss: 7.4506
Epoch 2/500
6/6 [==============================] - 2s 374ms/step - loss: 7.7076 - val_loss: 6.9214
Epoch 3/500
6/6 [==============================] - 2s 383ms/step - loss: 6.7585 - val_loss: 6.0563
Epoch 4/500
6/6 [==============================] - 2s 374ms/step - loss: 5.9905 - val_loss: 6.5141
Epoch 5/500
6/6 [==============================] - 2s 374ms/step - loss: 5.4644 - val_loss: 7.1316
Epoch 6/500
6/6 [==============================] - 2s 380ms/step - loss: 5.2311 - val_loss: 11.2576
Epoch 7/500
6/6 [==============================] - 2s 380ms/step - loss: 4.9663 - val_loss: 11.3040
Epoch 8/500
6/6 [==============================] - 3s 412ms/step - loss: 4.6796 - val_loss: 6.7173
Epoch 9/500
6/6 [==============================] - 2s 377ms/step - loss: 4.4720 - val_loss: 6.2206
Epoch 10/500
6/6 [==============================] - 2s 374ms/step - loss: 4.3592 - val_loss: 5.0863
Epoch 11/500
6/6 [==============================] - 2s 368ms/step - loss: 4.2198 - val_loss: 5.0051
Epoch 12/500
6/6 [==============================] - 2s 371ms/step - loss: 3.8580 - val_loss: 4.1607
Epoch 13/500
6/6 [==============================] - 2s 374ms/step - loss: 3.9814 - val_loss: 3.7642
Epoch 14/500
6/6 [==============================] - 2s 380ms/step - loss: 3.7562 - val_loss: 3.5621
Epoch 15/500
6/6 [==============================] - 2s 380ms/step - loss: 3.6786 - val_loss: 4.4084
Epoch 16/500
6/6 [==============================] - 2s 377ms/step - loss: 3.5540 - val_loss: 4.0831
Epoch 17/500
6/6 [==============================] - 2s 377ms/step - loss: 3.4120 - val_loss: 3.5206
Epoch 18/500
6/6 [==============================] - 2s 390ms/step - loss: 3.3688 - val_loss: 3.0939
Epoch 19/500
6/6 [==============================] - 2s 377ms/step - loss: 3.6056 - val_loss: 3.0495
Epoch 20/500
6/6 [==============================] - 2s 380ms/step - loss: 3.0518 - val_loss: 3.0127
Epoch 21/500
6/6 [==============================] - 2s 377ms/step - loss: 3.4549 - val_loss: 2.6066
Epoch 22/500
6/6 [==============================] - 2s 374ms/step - loss: 3.4406 - val_loss: 2.5944
Epoch 23/500
6/6 [==============================] - 2s 380ms/step - loss: 3.2990 - val_loss: 2.7624
Epoch 24/500
6/6 [==============================] - 2s 380ms/step - loss: 3.0017 - val_loss: 2.8741
Epoch 25/500
6/6 [==============================] - 2s 377ms/step - loss: 3.0767 - val_loss: 2.7455
Epoch 26/500
6/6 [==============================] - 2s 380ms/step - loss: 3.4782 - val_loss: 2.6058
Epoch 27/500
6/6 [==============================] - 2s 380ms/step - loss: 3.3045 - val_loss: 2.6558
Epoch 28/500
6/6 [==============================] - 2s 387ms/step - loss: 3.0558 - val_loss: 2.5938
Epoch 29/500
6/6 [==============================] - 2s 380ms/step - loss: 2.8675 - val_loss: 2.4482
Epoch 30/500
6/6 [==============================] - 2s 380ms/step - loss: 3.2269 - val_loss: 2.4834
Epoch 31/500
6/6 [==============================] - 2s 383ms/step - loss: 3.2295 - val_loss: 2.3993
Epoch 32/500
6/6 [==============================] - 2s 380ms/step - loss: 3.1182 - val_loss: 2.4113
Epoch 33/500
6/6 [==============================] - 2s 377ms/step - loss: 3.2522 - val_loss: 2.3555
Epoch 34/500
6/6 [==============================] - 2s 377ms/step - loss: 3.0658 - val_loss: 2.4332
Epoch 35/500
6/6 [==============================] - 2s 377ms/step - loss: 2.9815 - val_loss: 2.4398
Epoch 36/500
6/6 [==============================] - 2s 371ms/step - loss: 2.8759 - val_loss: 2.4503
Epoch 37/500
6/6 [==============================] - 2s 386ms/step - loss: 3.0037 - val_loss: 2.3781
Epoch 38/500
6/6 [==============================] - 2s 384ms/step - loss: 3.0319 - val_loss: 2.3202
Epoch 39/500
6/6 [==============================] - 2s 374ms/step - loss: 2.6809 - val_loss: 2.3487
Epoch 40/500
6/6 [==============================] - 2s 377ms/step - loss: 2.8456 - val_loss: 2.2716
Epoch 41/500
6/6 [==============================] - 2s 384ms/step - loss: 2.8612 - val_loss: 2.3141
Epoch 42/500
6/6 [==============================] - 2s 377ms/step - loss: 2.9631 - val_loss: 2.3309
Epoch 43/500
6/6 [==============================] - 2s 383ms/step - loss: 3.0288 - val_loss: 2.3560
Epoch 44/500
6/6 [==============================] - 2s 371ms/step - loss: 2.8620 - val_loss: 2.3740
Epoch 45/500
6/6 [==============================] - 2s 371ms/step - loss: 2.8304 - val_loss: 2.3008
Epoch 46/500
6/6 [==============================] - 2s 368ms/step - loss: 2.8361 - val_loss: 2.2852
Epoch 47/500
6/6 [==============================] - 2s 380ms/step - loss: 2.6307 - val_loss: 2.2348
Epoch 48/500
6/6 [==============================] - 2s 380ms/step - loss: 2.6610 - val_loss: 2.2620
Epoch 49/500
6/6 [==============================] - 2s 371ms/step - loss: 2.8631 - val_loss: 2.1886
Epoch 50/500
6/6 [==============================] - 2s 390ms/step - loss: 2.8242 - val_loss: 2.2405
Epoch 51/500
6/6 [==============================] - 3s 430ms/step - loss: 2.9297 - val_loss: 2.1604
Epoch 52/500
6/6 [==============================] - 2s 380ms/step - loss: 2.8378 - val_loss: 2.1928
Epoch 53/500
6/6 [==============================] - 2s 377ms/step - loss: 2.7484 - val_loss: 2.2761
Epoch 54/500
6/6 [==============================] - 2s 374ms/step - loss: 2.7676 - val_loss: 2.2807
Epoch 55/500
6/6 [==============================] - 2s 387ms/step - loss: 2.6554 - val_loss: 2.2192
Epoch 56/500
6/6 [==============================] - 2s 421ms/step - loss: 2.6234 - val_loss: 2.1519
Epoch 57/500
6/6 [==============================] - 2s 371ms/step - loss: 2.7592 - val_loss: 2.1821
Epoch 58/500
6/6 [==============================] - 2s 374ms/step - loss: 2.6490 - val_loss: 2.2146
Epoch 59/500
6/6 [==============================] - 2s 380ms/step - loss: 2.7587 - val_loss: 2.1594
Epoch 60/500
6/6 [==============================] - 2s 409ms/step - loss: 2.7723 - val_loss: 2.1355
Epoch 61/500
6/6 [==============================] - 2s 377ms/step - loss: 2.6975 - val_loss: 2.1791
Epoch 62/500
6/6 [==============================] - 2s 380ms/step - loss: 2.7561 - val_loss: 2.1987
Epoch 63/500
6/6 [==============================] - 2s 415ms/step - loss: 2.5916 - val_loss: 2.1296
Epoch 64/500
6/6 [==============================] - 2s 371ms/step - loss: 2.5259 - val_loss: 2.1619
Epoch 65/500
6/6 [==============================] - 2s 415ms/step - loss: 2.6447 - val_loss: 2.1236
Epoch 66/500
6/6 [==============================] - 2s 358ms/step - loss: 2.5903 - val_loss: 2.1698
Epoch 67/500
6/6 [==============================] - 2s 368ms/step - loss: 2.6661 - val_loss: 2.1512
Epoch 68/500
6/6 [==============================] - 2s 370ms/step - loss: 2.6885 - val_loss: 2.1264
Epoch 69/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5845 - val_loss: 2.1760
Epoch 70/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5923 - val_loss: 2.2162
Epoch 71/500
6/6 [==============================] - 2s 380ms/step - loss: 2.6383 - val_loss: 2.2537
Epoch 72/500
6/6 [==============================] - 2s 377ms/step - loss: 2.6810 - val_loss: 2.1946
Epoch 73/500
6/6 [==============================] - 2s 413ms/step - loss: 2.5956 - val_loss: 2.1169
Epoch 74/500
6/6 [==============================] - 3s 437ms/step - loss: 2.6807 - val_loss: 2.0478
Epoch 75/500
6/6 [==============================] - 2s 381ms/step - loss: 2.6920 - val_loss: 2.0630
Epoch 76/500
6/6 [==============================] - 2s 371ms/step - loss: 2.6100 - val_loss: 2.1032
Epoch 77/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5630 - val_loss: 2.0749
Epoch 78/500
6/6 [==============================] - 2s 383ms/step - loss: 2.6435 - val_loss: 2.0952
Epoch 79/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5537 - val_loss: 2.1113
Epoch 80/500
6/6 [==============================] - 2s 380ms/step - loss: 2.6424 - val_loss: 2.1222
Epoch 81/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5214 - val_loss: 2.1176
Epoch 82/500
6/6 [==============================] - 2s 371ms/step - loss: 2.6261 - val_loss: 2.0571
Epoch 83/500
6/6 [==============================] - 2s 409ms/step - loss: 2.5101 - val_loss: 2.0235
Epoch 84/500
6/6 [==============================] - 2s 371ms/step - loss: 2.4543 - val_loss: 2.0394
Epoch 85/500
6/6 [==============================] - 2s 377ms/step - loss: 2.4958 - val_loss: 2.1148
Epoch 86/500
6/6 [==============================] - 2s 377ms/step - loss: 2.6478 - val_loss: 2.0862
Epoch 87/500
6/6 [==============================] - 2s 377ms/step - loss: 2.6153 - val_loss: 2.0523
Epoch 88/500
6/6 [==============================] - 2s 416ms/step - loss: 2.3568 - val_loss: 1.9924
Epoch 89/500
6/6 [==============================] - 2s 410ms/step - loss: 2.6164 - val_loss: 1.9757
Epoch 90/500
6/6 [==============================] - 2s 375ms/step - loss: 2.5430 - val_loss: 2.0674
Epoch 91/500
6/6 [==============================] - 2s 368ms/step - loss: 2.6248 - val_loss: 2.0810
Epoch 92/500
6/6 [==============================] - 2s 374ms/step - loss: 2.3781 - val_loss: 2.1113
Epoch 93/500
6/6 [==============================] - 2s 387ms/step - loss: 2.6603 - val_loss: 2.1442
Epoch 94/500
6/6 [==============================] - 2s 377ms/step - loss: 2.5129 - val_loss: 2.1626
Epoch 95/500
6/6 [==============================] - 2s 374ms/step - loss: 2.3896 - val_loss: 2.1489
Epoch 96/500
6/6 [==============================] - 2s 371ms/step - loss: 2.6327 - val_loss: 2.0828
Epoch 97/500
6/6 [==============================] - 2s 371ms/step - loss: 2.6187 - val_loss: 2.0910
Epoch 98/500
6/6 [==============================] - 2s 365ms/step - loss: 2.5146 - val_loss: 2.0742
Epoch 99/500
6/6 [==============================] - 2s 371ms/step - loss: 2.3130 - val_loss: 2.0514
Epoch 100/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4038 - val_loss: 2.0552
Epoch 101/500
6/6 [==============================] - 2s 380ms/step - loss: 2.4212 - val_loss: 2.0109
Epoch 102/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5340 - val_loss: 1.9892
Epoch 103/500
6/6 [==============================] - 2s 374ms/step - loss: 2.3717 - val_loss: 2.0015
Epoch 104/500
6/6 [==============================] - 2s 375ms/step - loss: 2.4715 - val_loss: 2.0293
Epoch 105/500
6/6 [==============================] - 2s 368ms/step - loss: 2.4663 - val_loss: 2.0629
Epoch 106/500
6/6 [==============================] - 2s 384ms/step - loss: 2.4670 - val_loss: 2.0257
Epoch 107/500
6/6 [==============================] - 2s 365ms/step - loss: 2.5104 - val_loss: 2.0081
Epoch 108/500
6/6 [==============================] - 2s 377ms/step - loss: 2.4865 - val_loss: 2.0538
Epoch 109/500
6/6 [==============================] - 3s 537ms/step - loss: 2.4713 - val_loss: 2.0605
Epoch 109: early stopping
Epoch 1/500
6/6 [==============================] - 36s 795ms/step - loss: 3.8935 - val_loss: 2.0414
Epoch 2/500
6/6 [==============================] - 3s 443ms/step - loss: 3.2845 - val_loss: 2.1932
Epoch 3/500
6/6 [==============================] - 3s 452ms/step - loss: 2.7260 - val_loss: 2.3661
Epoch 4/500
6/6 [==============================] - 3s 411ms/step - loss: 2.6751 - val_loss: 2.4471
Epoch 5/500
6/6 [==============================] - 3s 439ms/step - loss: 2.2776 - val_loss: 2.5334
Epoch 6/500
6/6 [==============================] - 3s 486ms/step - loss: 2.2411 - val_loss: 2.4587
Epoch 7/500
6/6 [==============================] - 3s 476ms/step - loss: 2.0764 - val_loss: 2.2786
Epoch 8/500
6/6 [==============================] - 3s 468ms/step - loss: 2.1024 - val_loss: 2.2363
Epoch 9/500
6/6 [==============================] - 3s 451ms/step - loss: 2.0799 - val_loss: 2.2432
Epoch 10/500
6/6 [==============================] - 3s 450ms/step - loss: 2.0626 - val_loss: 2.2372
Epoch 11/500
6/6 [==============================] - 3s 493ms/step - loss: 2.1077 - val_loss: 2.1697
Epoch 12/500
6/6 [==============================] - 3s 490ms/step - loss: 2.0142 - val_loss: 2.1489
Epoch 13/500
6/6 [==============================] - 3s 456ms/step - loss: 1.8060 - val_loss: 2.1109
Epoch 14/500
6/6 [==============================] - 3s 499ms/step - loss: 1.9273 - val_loss: 1.9890
Epoch 15/500
6/6 [==============================] - 3s 433ms/step - loss: 1.9559 - val_loss: 2.0293
Epoch 16/500
6/6 [==============================] - 3s 493ms/step - loss: 1.7736 - val_loss: 1.9281
Epoch 17/500
6/6 [==============================] - 3s 503ms/step - loss: 1.8415 - val_loss: 1.8370
Epoch 18/500
6/6 [==============================] - 3s 499ms/step - loss: 1.7337 - val_loss: 1.7762
Epoch 19/500
6/6 [==============================] - 3s 486ms/step - loss: 1.9110 - val_loss: 1.7226
Epoch 20/500
6/6 [==============================] - 3s 478ms/step - loss: 1.7517 - val_loss: 1.7273
Epoch 21/500
6/6 [==============================] - 3s 455ms/step - loss: 1.6818 - val_loss: 1.7904
Epoch 22/500
6/6 [==============================] - 3s 480ms/step - loss: 1.5668 - val_loss: 1.8066
Epoch 23/500
6/6 [==============================] - 3s 484ms/step - loss: 1.6695 - val_loss: 1.7377
Epoch 24/500
6/6 [==============================] - 3s 470ms/step - loss: 1.7700 - val_loss: 1.7794
Epoch 25/500
6/6 [==============================] - 3s 476ms/step - loss: 1.6655 - val_loss: 1.7802
Epoch 26/500
6/6 [==============================] - 3s 464ms/step - loss: 1.7978 - val_loss: 1.7479
Epoch 27/500
6/6 [==============================] - 3s 490ms/step - loss: 1.6809 - val_loss: 1.7625
Epoch 28/500
6/6 [==============================] - 3s 478ms/step - loss: 1.7930 - val_loss: 1.7738
Epoch 29/500
6/6 [==============================] - 3s 515ms/step - loss: 1.5966 - val_loss: 1.6937
Epoch 30/500
6/6 [==============================] - 3s 495ms/step - loss: 1.6597 - val_loss: 1.6881
Epoch 31/500
6/6 [==============================] - 3s 480ms/step - loss: 1.7045 - val_loss: 1.6931
Epoch 32/500
6/6 [==============================] - 3s 535ms/step - loss: 1.6874 - val_loss: 1.6640
Epoch 33/500
6/6 [==============================] - 3s 511ms/step - loss: 1.5655 - val_loss: 1.6458
Epoch 34/500
6/6 [==============================] - 3s 508ms/step - loss: 1.6127 - val_loss: 1.6396
Epoch 35/500
6/6 [==============================] - 3s 520ms/step - loss: 1.4765 - val_loss: 1.6154
Epoch 36/500
6/6 [==============================] - 3s 511ms/step - loss: 1.4174 - val_loss: 1.5911
Epoch 37/500
6/6 [==============================] - 3s 489ms/step - loss: 1.4870 - val_loss: 1.5834
Epoch 38/500
6/6 [==============================] - 3s 496ms/step - loss: 1.4859 - val_loss: 1.5808
Epoch 39/500
6/6 [==============================] - 3s 502ms/step - loss: 1.6464 - val_loss: 1.5663
Epoch 40/500
6/6 [==============================] - 3s 481ms/step - loss: 1.5395 - val_loss: 1.5990
Epoch 41/500
6/6 [==============================] - 3s 451ms/step - loss: 1.4020 - val_loss: 1.5897
Epoch 42/500
6/6 [==============================] - 3s 474ms/step - loss: 1.5261 - val_loss: 1.5993
Epoch 43/500
6/6 [==============================] - 3s 477ms/step - loss: 1.5536 - val_loss: 1.5768
Epoch 44/500
6/6 [==============================] - 3s 478ms/step - loss: 1.4741 - val_loss: 1.5689
Epoch 45/500
6/6 [==============================] - 3s 463ms/step - loss: 1.4800 - val_loss: 1.5671
Epoch 46/500
6/6 [==============================] - 3s 526ms/step - loss: 1.4980 - val_loss: 1.5357
Epoch 47/500
6/6 [==============================] - 3s 499ms/step - loss: 1.3982 - val_loss: 1.5292
Epoch 48/500
6/6 [==============================] - 3s 526ms/step - loss: 1.4673 - val_loss: 1.5259
Epoch 49/500
6/6 [==============================] - 3s 478ms/step - loss: 1.4058 - val_loss: 1.5553
Epoch 50/500
6/6 [==============================] - 3s 499ms/step - loss: 1.3262 - val_loss: 1.6060
Epoch 51/500
6/6 [==============================] - 3s 472ms/step - loss: 1.3092 - val_loss: 1.5417
Epoch 52/500
6/6 [==============================] - 3s 472ms/step - loss: 1.3619 - val_loss: 1.5642
Epoch 53/500
6/6 [==============================] - 3s 467ms/step - loss: 1.4305 - val_loss: 1.5584
Epoch 54/500
6/6 [==============================] - 3s 459ms/step - loss: 1.5015 - val_loss: 1.5666
Epoch 55/500
6/6 [==============================] - 3s 469ms/step - loss: 1.6680 - val_loss: 1.5825
Epoch 56/500
6/6 [==============================] - 3s 492ms/step - loss: 1.4189 - val_loss: 1.5515
Epoch 57/500
6/6 [==============================] - 3s 485ms/step - loss: 1.3790 - val_loss: 1.5482
Epoch 58/500
6/6 [==============================] - 3s 490ms/step - loss: 1.3227 - val_loss: 1.5400
Epoch 59/500
6/6 [==============================] - 3s 490ms/step - loss: 1.3669 - val_loss: 1.5519
Epoch 60/500
6/6 [==============================] - 3s 473ms/step - loss: 1.4365 - val_loss: 1.5489
Epoch 61/500
6/6 [==============================] - 3s 476ms/step - loss: 1.2996 - val_loss: 1.5641
Epoch 62/500
6/6 [==============================] - 3s 525ms/step - loss: 1.5809 - val_loss: 1.5186
Epoch 63/500
6/6 [==============================] - 3s 473ms/step - loss: 1.4428 - val_loss: 1.5424
Epoch 64/500
6/6 [==============================] - 3s 478ms/step - loss: 1.5477 - val_loss: 1.5497
Epoch 65/500
6/6 [==============================] - 3s 477ms/step - loss: 1.3340 - val_loss: 1.5838
Epoch 66/500
6/6 [==============================] - 3s 488ms/step - loss: 1.4436 - val_loss: 1.5793
Epoch 67/500
6/6 [==============================] - 3s 482ms/step - loss: 1.4312 - val_loss: 1.6057
Epoch 68/500
6/6 [==============================] - 3s 472ms/step - loss: 1.4199 - val_loss: 1.5603
Epoch 69/500
6/6 [==============================] - 3s 487ms/step - loss: 1.4183 - val_loss: 1.5223
Epoch 70/500
6/6 [==============================] - 3s 519ms/step - loss: 1.3984 - val_loss: 1.5166
Epoch 71/500
6/6 [==============================] - 3s 473ms/step - loss: 1.3462 - val_loss: 1.5275
Epoch 72/500
6/6 [==============================] - 3s 476ms/step - loss: 1.3630 - val_loss: 1.5826
Epoch 73/500
6/6 [==============================] - 3s 478ms/step - loss: 1.3944 - val_loss: 1.5592
Epoch 74/500
6/6 [==============================] - 3s 485ms/step - loss: 1.2160 - val_loss: 1.5664
Epoch 75/500
6/6 [==============================] - 3s 524ms/step - loss: 1.3827 - val_loss: 1.4883
Epoch 76/500
6/6 [==============================] - 3s 516ms/step - loss: 1.2832 - val_loss: 1.4470
Epoch 77/500
6/6 [==============================] - 3s 495ms/step - loss: 1.4135 - val_loss: 1.4523
Epoch 78/500
6/6 [==============================] - 3s 537ms/step - loss: 1.2829 - val_loss: 1.4400
Epoch 79/500
6/6 [==============================] - 3s 470ms/step - loss: 1.2750 - val_loss: 1.4711
Epoch 80/500
6/6 [==============================] - 3s 483ms/step - loss: 1.4058 - val_loss: 1.4634
Epoch 81/500
6/6 [==============================] - 3s 478ms/step - loss: 1.2526 - val_loss: 1.4795
Epoch 82/500
6/6 [==============================] - 3s 484ms/step - loss: 1.3838 - val_loss: 1.4546
Epoch 83/500
6/6 [==============================] - 3s 475ms/step - loss: 1.4073 - val_loss: 1.4857
Epoch 84/500
6/6 [==============================] - 3s 481ms/step - loss: 1.2847 - val_loss: 1.4828
Epoch 85/500
6/6 [==============================] - 3s 480ms/step - loss: 1.3252 - val_loss: 1.5010
Epoch 86/500
6/6 [==============================] - 3s 485ms/step - loss: 1.2314 - val_loss: 1.4698
Epoch 87/500
6/6 [==============================] - 3s 479ms/step - loss: 1.3964 - val_loss: 1.4734
Epoch 88/500
6/6 [==============================] - 3s 485ms/step - loss: 1.2652 - val_loss: 1.4593
Epoch 89/500
6/6 [==============================] - 3s 500ms/step - loss: 1.3682 - val_loss: 1.4474
Epoch 90/500
6/6 [==============================] - 3s 490ms/step - loss: 1.2210 - val_loss: 1.4427
Epoch 91/500
6/6 [==============================] - 3s 485ms/step - loss: 1.3521 - val_loss: 1.4928
Epoch 92/500
6/6 [==============================] - 3s 475ms/step - loss: 1.3119 - val_loss: 1.5038
Epoch 93/500
6/6 [==============================] - 3s 491ms/step - loss: 1.1781 - val_loss: 1.4767
Epoch 94/500
6/6 [==============================] - 3s 487ms/step - loss: 1.3113 - val_loss: 1.4405
Epoch 95/500
6/6 [==============================] - 3s 485ms/step - loss: 1.2063 - val_loss: 1.4807
Epoch 96/500
6/6 [==============================] - 3s 478ms/step - loss: 1.3465 - val_loss: 1.4987
Epoch 97/500
6/6 [==============================] - 3s 486ms/step - loss: 1.2113 - val_loss: 1.4993
Epoch 98/500
6/6 [==============================] - 5s 772ms/step - loss: 1.1881 - val_loss: 1.4747
Epoch 98: early stopping
Epoch 1/500
6/6 [==============================] - 11s 707ms/step - loss: 9.9242 - val_loss: 10.1331
Epoch 2/500
6/6 [==============================] - 2s 405ms/step - loss: 8.0328 - val_loss: 9.3248
Epoch 3/500
6/6 [==============================] - 2s 387ms/step - loss: 6.5667 - val_loss: 8.2314
Epoch 4/500
6/6 [==============================] - 2s 380ms/step - loss: 6.3664 - val_loss: 6.4951
Epoch 5/500
6/6 [==============================] - 2s 377ms/step - loss: 5.5794 - val_loss: 5.6872
Epoch 6/500
6/6 [==============================] - 2s 387ms/step - loss: 5.0693 - val_loss: 4.9427
Epoch 7/500
6/6 [==============================] - 2s 396ms/step - loss: 4.7740 - val_loss: 4.2041
Epoch 8/500
6/6 [==============================] - 2s 383ms/step - loss: 4.7732 - val_loss: 5.5224
Epoch 9/500
6/6 [==============================] - 2s 390ms/step - loss: 4.7278 - val_loss: 5.5853
Epoch 10/500
6/6 [==============================] - 2s 380ms/step - loss: 4.4721 - val_loss: 4.3428
Epoch 11/500
6/6 [==============================] - 2s 384ms/step - loss: 4.3993 - val_loss: 3.4584
Epoch 12/500
6/6 [==============================] - 2s 398ms/step - loss: 4.1703 - val_loss: 3.4159
Epoch 13/500
6/6 [==============================] - 2s 383ms/step - loss: 4.2183 - val_loss: 3.8435
Epoch 14/500
6/6 [==============================] - 2s 376ms/step - loss: 3.9466 - val_loss: 3.6131
Epoch 15/500
6/6 [==============================] - 2s 390ms/step - loss: 3.8470 - val_loss: 3.7418
Epoch 16/500
6/6 [==============================] - 2s 396ms/step - loss: 3.8342 - val_loss: 3.5309
Epoch 17/500
6/6 [==============================] - 2s 393ms/step - loss: 3.5496 - val_loss: 3.6037
Epoch 18/500
6/6 [==============================] - 2s 384ms/step - loss: 3.5162 - val_loss: 3.2775
Epoch 19/500
6/6 [==============================] - 2s 381ms/step - loss: 3.6197 - val_loss: 3.1325
Epoch 20/500
6/6 [==============================] - 2s 393ms/step - loss: 3.4155 - val_loss: 3.1457
Epoch 21/500
6/6 [==============================] - 2s 378ms/step - loss: 3.3996 - val_loss: 2.9277
Epoch 22/500
6/6 [==============================] - 2s 384ms/step - loss: 3.3070 - val_loss: 2.9583
Epoch 23/500
6/6 [==============================] - 2s 374ms/step - loss: 3.1847 - val_loss: 2.9421
Epoch 24/500
6/6 [==============================] - 2s 387ms/step - loss: 3.1424 - val_loss: 2.9199
Epoch 25/500
6/6 [==============================] - 2s 393ms/step - loss: 3.0376 - val_loss: 3.0410
Epoch 26/500
6/6 [==============================] - 2s 385ms/step - loss: 3.3369 - val_loss: 3.1753
Epoch 27/500
6/6 [==============================] - 2s 384ms/step - loss: 3.0804 - val_loss: 3.1360
Epoch 28/500
6/6 [==============================] - 2s 384ms/step - loss: 3.2688 - val_loss: 2.8530
Epoch 29/500
6/6 [==============================] - 2s 387ms/step - loss: 2.9799 - val_loss: 2.7137
Epoch 30/500
6/6 [==============================] - 2s 384ms/step - loss: 3.2306 - val_loss: 2.7739
Epoch 31/500
6/6 [==============================] - 2s 381ms/step - loss: 3.3187 - val_loss: 2.6571
Epoch 32/500
6/6 [==============================] - 2s 384ms/step - loss: 3.1969 - val_loss: 2.5343
Epoch 33/500
6/6 [==============================] - 2s 387ms/step - loss: 2.9907 - val_loss: 2.6177
Epoch 34/500
6/6 [==============================] - 2s 387ms/step - loss: 2.8812 - val_loss: 2.5683
Epoch 35/500
6/6 [==============================] - 2s 390ms/step - loss: 3.0215 - val_loss: 2.5102
Epoch 36/500
6/6 [==============================] - 2s 378ms/step - loss: 2.9559 - val_loss: 2.4791
Epoch 37/500
6/6 [==============================] - 2s 393ms/step - loss: 2.8490 - val_loss: 2.2738
Epoch 38/500
6/6 [==============================] - 2s 381ms/step - loss: 2.8940 - val_loss: 2.3115
Epoch 39/500
6/6 [==============================] - 2s 390ms/step - loss: 2.7981 - val_loss: 2.3338
Epoch 40/500
6/6 [==============================] - 2s 377ms/step - loss: 3.1363 - val_loss: 2.2443
Epoch 41/500
6/6 [==============================] - 2s 387ms/step - loss: 3.0007 - val_loss: 2.1590
Epoch 42/500
6/6 [==============================] - 2s 390ms/step - loss: 3.0012 - val_loss: 2.1857
Epoch 43/500
6/6 [==============================] - 2s 388ms/step - loss: 2.8144 - val_loss: 2.1625
Epoch 44/500
6/6 [==============================] - 2s 384ms/step - loss: 2.8555 - val_loss: 2.2242
Epoch 45/500
6/6 [==============================] - 2s 390ms/step - loss: 2.8411 - val_loss: 2.2307
Epoch 46/500
6/6 [==============================] - 2s 390ms/step - loss: 2.8173 - val_loss: 2.1998
Epoch 47/500
6/6 [==============================] - 2s 393ms/step - loss: 2.8784 - val_loss: 2.1682
Epoch 48/500
6/6 [==============================] - 2s 377ms/step - loss: 2.9855 - val_loss: 2.1675
Epoch 49/500
6/6 [==============================] - 2s 390ms/step - loss: 2.8336 - val_loss: 2.2249
Epoch 50/500
6/6 [==============================] - 2s 390ms/step - loss: 2.9671 - val_loss: 2.1680
Epoch 51/500
6/6 [==============================] - 3s 450ms/step - loss: 2.8349 - val_loss: 2.1915
Epoch 52/500
6/6 [==============================] - 2s 371ms/step - loss: 2.9371 - val_loss: 2.2299
Epoch 53/500
6/6 [==============================] - 2s 386ms/step - loss: 2.7490 - val_loss: 2.2878
Epoch 54/500
6/6 [==============================] - 2s 392ms/step - loss: 2.7626 - val_loss: 2.2463
Epoch 55/500
6/6 [==============================] - 3s 438ms/step - loss: 2.7810 - val_loss: 2.1850
Epoch 56/500
6/6 [==============================] - 2s 379ms/step - loss: 2.8464 - val_loss: 2.2002
Epoch 57/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6803 - val_loss: 2.2360
Epoch 58/500
6/6 [==============================] - 2s 400ms/step - loss: 2.9759 - val_loss: 2.2130
Epoch 59/500
6/6 [==============================] - 2s 385ms/step - loss: 2.6903 - val_loss: 2.1968
Epoch 60/500
6/6 [==============================] - 2s 389ms/step - loss: 2.7784 - val_loss: 2.2235
Epoch 61/500
6/6 [==============================] - 3s 433ms/step - loss: 2.7964 - val_loss: 2.1440
Epoch 62/500
6/6 [==============================] - 2s 380ms/step - loss: 2.7186 - val_loss: 2.1555
Epoch 63/500
6/6 [==============================] - 3s 414ms/step - loss: 2.7460 - val_loss: 2.1270
Epoch 64/500
6/6 [==============================] - 2s 415ms/step - loss: 2.6279 - val_loss: 2.1199
Epoch 65/500
6/6 [==============================] - 3s 425ms/step - loss: 2.7985 - val_loss: 2.1095
Epoch 66/500
6/6 [==============================] - 2s 387ms/step - loss: 2.7282 - val_loss: 2.2731
Epoch 67/500
6/6 [==============================] - 3s 428ms/step - loss: 2.6774 - val_loss: 2.1030
Epoch 68/500
6/6 [==============================] - 2s 384ms/step - loss: 2.6002 - val_loss: 2.1125
Epoch 69/500
6/6 [==============================] - 2s 385ms/step - loss: 2.8299 - val_loss: 2.1283
Epoch 70/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6630 - val_loss: 2.1241
Epoch 71/500
6/6 [==============================] - 2s 378ms/step - loss: 2.4604 - val_loss: 2.1180
Epoch 72/500
6/6 [==============================] - 2s 387ms/step - loss: 2.6697 - val_loss: 2.1201
Epoch 73/500
6/6 [==============================] - 3s 422ms/step - loss: 2.5530 - val_loss: 2.0529
Epoch 74/500
6/6 [==============================] - 3s 431ms/step - loss: 2.7342 - val_loss: 2.0186
Epoch 75/500
6/6 [==============================] - 2s 384ms/step - loss: 2.7172 - val_loss: 2.0509
Epoch 76/500
6/6 [==============================] - 2s 381ms/step - loss: 2.7390 - val_loss: 2.0841
Epoch 77/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6540 - val_loss: 2.1033
Epoch 78/500
6/6 [==============================] - 2s 381ms/step - loss: 2.6688 - val_loss: 2.2187
Epoch 79/500
6/6 [==============================] - 2s 383ms/step - loss: 2.8179 - val_loss: 2.1492
Epoch 80/500
6/6 [==============================] - 2s 384ms/step - loss: 2.5746 - val_loss: 2.2032
Epoch 81/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5689 - val_loss: 2.1453
Epoch 82/500
6/6 [==============================] - 2s 387ms/step - loss: 2.4347 - val_loss: 2.1416
Epoch 83/500
6/6 [==============================] - 2s 384ms/step - loss: 2.4348 - val_loss: 2.1120
Epoch 84/500
6/6 [==============================] - 2s 387ms/step - loss: 2.4525 - val_loss: 2.1644
Epoch 85/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5742 - val_loss: 2.1122
Epoch 86/500
6/6 [==============================] - 2s 381ms/step - loss: 2.6531 - val_loss: 2.0237
Epoch 87/500
6/6 [==============================] - 3s 419ms/step - loss: 2.6489 - val_loss: 1.9837
Epoch 88/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5507 - val_loss: 2.0341
Epoch 89/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5403 - val_loss: 2.0463
Epoch 90/500
6/6 [==============================] - 2s 384ms/step - loss: 2.5694 - val_loss: 2.0536
Epoch 91/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5263 - val_loss: 2.0512
Epoch 92/500
6/6 [==============================] - 2s 381ms/step - loss: 2.6950 - val_loss: 2.0510
Epoch 93/500
6/6 [==============================] - 2s 393ms/step - loss: 2.3931 - val_loss: 2.0441
Epoch 94/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5742 - val_loss: 2.0622
Epoch 95/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6362 - val_loss: 2.0348
Epoch 96/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6090 - val_loss: 2.0064
Epoch 97/500
6/6 [==============================] - 2s 394ms/step - loss: 2.6827 - val_loss: 2.0641
Epoch 98/500
6/6 [==============================] - 2s 381ms/step - loss: 2.4464 - val_loss: 2.0501
Epoch 99/500
6/6 [==============================] - 2s 397ms/step - loss: 2.5607 - val_loss: 2.0576
Epoch 100/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5173 - val_loss: 2.0658
Epoch 101/500
6/6 [==============================] - 2s 359ms/step - loss: 2.4819 - val_loss: 2.0729
Epoch 102/500
6/6 [==============================] - 2s 409ms/step - loss: 2.4765 - val_loss: 2.0679
Epoch 103/500
6/6 [==============================] - 2s 396ms/step - loss: 2.5653 - val_loss: 2.0643
Epoch 104/500
6/6 [==============================] - 2s 381ms/step - loss: 2.6028 - val_loss: 2.0582
Epoch 105/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4852 - val_loss: 2.0144
Epoch 106/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4374 - val_loss: 2.0706
Epoch 107/500
6/6 [==============================] - 3s 557ms/step - loss: 2.3814 - val_loss: 1.9980
Epoch 107: early stopping
Epoch 1/500
6/6 [==============================] - 36s 809ms/step - loss: 3.7136 - val_loss: 1.9540
Epoch 2/500
6/6 [==============================] - 3s 463ms/step - loss: 3.2535 - val_loss: 2.0590
Epoch 3/500
6/6 [==============================] - 3s 437ms/step - loss: 2.8184 - val_loss: 2.2889
Epoch 4/500
6/6 [==============================] - 3s 463ms/step - loss: 2.4469 - val_loss: 2.2508
Epoch 5/500
6/6 [==============================] - 3s 444ms/step - loss: 2.3687 - val_loss: 2.1593
Epoch 6/500
6/6 [==============================] - 3s 486ms/step - loss: 2.1690 - val_loss: 2.2239
Epoch 7/500
6/6 [==============================] - 3s 456ms/step - loss: 2.2250 - val_loss: 2.2524
Epoch 8/500
6/6 [==============================] - 3s 478ms/step - loss: 2.0698 - val_loss: 2.1358
Epoch 9/500
6/6 [==============================] - 3s 506ms/step - loss: 2.0153 - val_loss: 2.0334
Epoch 10/500
6/6 [==============================] - 3s 486ms/step - loss: 2.0170 - val_loss: 2.0011
Epoch 11/500
6/6 [==============================] - 3s 484ms/step - loss: 2.0545 - val_loss: 1.9415
Epoch 12/500
6/6 [==============================] - 3s 467ms/step - loss: 1.9267 - val_loss: 1.9638
Epoch 13/500
6/6 [==============================] - 3s 501ms/step - loss: 1.9792 - val_loss: 1.9787
Epoch 14/500
6/6 [==============================] - 3s 463ms/step - loss: 1.8526 - val_loss: 1.9510
Epoch 15/500
6/6 [==============================] - 3s 540ms/step - loss: 1.8502 - val_loss: 1.9208
Epoch 16/500
6/6 [==============================] - 3s 484ms/step - loss: 1.8799 - val_loss: 1.8778
Epoch 17/500
6/6 [==============================] - 3s 440ms/step - loss: 1.7445 - val_loss: 1.9297
Epoch 18/500
6/6 [==============================] - 3s 487ms/step - loss: 1.9049 - val_loss: 1.9960
Epoch 19/500
6/6 [==============================] - 3s 489ms/step - loss: 1.6461 - val_loss: 2.0110
Epoch 20/500
6/6 [==============================] - 3s 504ms/step - loss: 1.6743 - val_loss: 2.0614
Epoch 21/500
6/6 [==============================] - 3s 490ms/step - loss: 1.7629 - val_loss: 2.0195
Epoch 22/500
6/6 [==============================] - 3s 476ms/step - loss: 1.7032 - val_loss: 1.9510
Epoch 23/500
6/6 [==============================] - 3s 476ms/step - loss: 1.6629 - val_loss: 1.9215
Epoch 24/500
6/6 [==============================] - 3s 493ms/step - loss: 1.8314 - val_loss: 1.9147
Epoch 25/500
6/6 [==============================] - 3s 522ms/step - loss: 1.6154 - val_loss: 1.7712
Epoch 26/500
6/6 [==============================] - 3s 510ms/step - loss: 1.6641 - val_loss: 1.6789
Epoch 27/500
6/6 [==============================] - 3s 534ms/step - loss: 1.7368 - val_loss: 1.6598
Epoch 28/500
6/6 [==============================] - 3s 506ms/step - loss: 1.6001 - val_loss: 1.6462
Epoch 29/500
6/6 [==============================] - 3s 471ms/step - loss: 1.6980 - val_loss: 1.6583
Epoch 30/500
6/6 [==============================] - 3s 530ms/step - loss: 1.6205 - val_loss: 1.6449
Epoch 31/500
6/6 [==============================] - 3s 466ms/step - loss: 1.4823 - val_loss: 1.6825
Epoch 32/500
6/6 [==============================] - 3s 490ms/step - loss: 1.5291 - val_loss: 1.7461
Epoch 33/500
6/6 [==============================] - 3s 465ms/step - loss: 1.7459 - val_loss: 1.7293
Epoch 34/500
6/6 [==============================] - 3s 482ms/step - loss: 1.6341 - val_loss: 1.7417
Epoch 35/500
6/6 [==============================] - 3s 512ms/step - loss: 1.5066 - val_loss: 1.6907
Epoch 36/500
6/6 [==============================] - 3s 491ms/step - loss: 1.7263 - val_loss: 1.6543
Epoch 37/500
6/6 [==============================] - 3s 472ms/step - loss: 1.5196 - val_loss: 1.6663
Epoch 38/500
6/6 [==============================] - 3s 532ms/step - loss: 1.4593 - val_loss: 1.5812
Epoch 39/500
6/6 [==============================] - 3s 518ms/step - loss: 1.4813 - val_loss: 1.5705
Epoch 40/500
6/6 [==============================] - 3s 518ms/step - loss: 1.5387 - val_loss: 1.5278
Epoch 41/500
6/6 [==============================] - 3s 519ms/step - loss: 1.5520 - val_loss: 1.4970
Epoch 42/500
6/6 [==============================] - 3s 488ms/step - loss: 1.5748 - val_loss: 1.5054
Epoch 43/500
6/6 [==============================] - 3s 457ms/step - loss: 1.5394 - val_loss: 1.5145
Epoch 44/500
6/6 [==============================] - 3s 500ms/step - loss: 1.5541 - val_loss: 1.5031
Epoch 45/500
6/6 [==============================] - 3s 479ms/step - loss: 1.6016 - val_loss: 1.6026
Epoch 46/500
6/6 [==============================] - 3s 509ms/step - loss: 1.5476 - val_loss: 1.6272
Epoch 47/500
6/6 [==============================] - 3s 477ms/step - loss: 1.5581 - val_loss: 1.6219
Epoch 48/500
6/6 [==============================] - 3s 487ms/step - loss: 1.3753 - val_loss: 1.6062
Epoch 49/500
6/6 [==============================] - 3s 483ms/step - loss: 1.4458 - val_loss: 1.5796
Epoch 50/500
6/6 [==============================] - 3s 485ms/step - loss: 1.4685 - val_loss: 1.5391
Epoch 51/500
6/6 [==============================] - 3s 482ms/step - loss: 1.4831 - val_loss: 1.5230
Epoch 52/500
6/6 [==============================] - 3s 498ms/step - loss: 1.4703 - val_loss: 1.5631
Epoch 53/500
6/6 [==============================] - 3s 501ms/step - loss: 1.5789 - val_loss: 1.5686
Epoch 54/500
6/6 [==============================] - 3s 501ms/step - loss: 1.3691 - val_loss: 1.5573
Epoch 55/500
6/6 [==============================] - 3s 506ms/step - loss: 1.5395 - val_loss: 1.5088
Epoch 56/500
6/6 [==============================] - 3s 491ms/step - loss: 1.4404 - val_loss: 1.5280
Epoch 57/500
6/6 [==============================] - 3s 493ms/step - loss: 1.5249 - val_loss: 1.5137
Epoch 58/500
6/6 [==============================] - 3s 492ms/step - loss: 1.5482 - val_loss: 1.5629
Epoch 59/500
6/6 [==============================] - 3s 477ms/step - loss: 1.4917 - val_loss: 1.5706
Epoch 60/500
6/6 [==============================] - 3s 500ms/step - loss: 1.4808 - val_loss: 1.5362
Epoch 61/500
6/6 [==============================] - 5s 786ms/step - loss: 1.4776 - val_loss: 1.5567
Epoch 61: early stopping
Epoch 1/500
6/6 [==============================] - 11s 706ms/step - loss: 10.1267 - val_loss: 5.6775
Epoch 2/500
6/6 [==============================] - 2s 383ms/step - loss: 7.6597 - val_loss: 6.6654
Epoch 3/500
6/6 [==============================] - 2s 380ms/step - loss: 7.1167 - val_loss: 5.9000
Epoch 4/500
6/6 [==============================] - 2s 380ms/step - loss: 6.3755 - val_loss: 5.4012
Epoch 5/500
6/6 [==============================] - 2s 377ms/step - loss: 5.4501 - val_loss: 4.5025
Epoch 6/500
6/6 [==============================] - 2s 392ms/step - loss: 5.3117 - val_loss: 4.9500
Epoch 7/500
6/6 [==============================] - 2s 383ms/step - loss: 5.1868 - val_loss: 5.4733
Epoch 8/500
6/6 [==============================] - 2s 380ms/step - loss: 4.5648 - val_loss: 5.0528
Epoch 9/500
6/6 [==============================] - 2s 405ms/step - loss: 4.8042 - val_loss: 4.1677
Epoch 10/500
6/6 [==============================] - 2s 380ms/step - loss: 4.0992 - val_loss: 4.4870
Epoch 11/500
6/6 [==============================] - 2s 386ms/step - loss: 4.1721 - val_loss: 4.1771
Epoch 12/500
6/6 [==============================] - 2s 380ms/step - loss: 4.0564 - val_loss: 4.0416
Epoch 13/500
6/6 [==============================] - 2s 386ms/step - loss: 3.7627 - val_loss: 3.6993
Epoch 14/500
6/6 [==============================] - 2s 396ms/step - loss: 3.7415 - val_loss: 3.7248
Epoch 15/500
6/6 [==============================] - 2s 393ms/step - loss: 3.9546 - val_loss: 3.9648
Epoch 16/500
6/6 [==============================] - 2s 393ms/step - loss: 3.7339 - val_loss: 4.0098
Epoch 17/500
6/6 [==============================] - 2s 389ms/step - loss: 3.5836 - val_loss: 3.6516
Epoch 18/500
6/6 [==============================] - 2s 383ms/step - loss: 3.5251 - val_loss: 3.3718
Epoch 19/500
6/6 [==============================] - 2s 389ms/step - loss: 3.3267 - val_loss: 2.9535
Epoch 20/500
6/6 [==============================] - 2s 386ms/step - loss: 3.3555 - val_loss: 2.8032
Epoch 21/500
6/6 [==============================] - 2s 392ms/step - loss: 3.4721 - val_loss: 2.7590
Epoch 22/500
6/6 [==============================] - 2s 383ms/step - loss: 3.3954 - val_loss: 2.8535
Epoch 23/500
6/6 [==============================] - 2s 381ms/step - loss: 3.5731 - val_loss: 2.7092
Epoch 24/500
6/6 [==============================] - 2s 399ms/step - loss: 3.2567 - val_loss: 2.5098
Epoch 25/500
6/6 [==============================] - 2s 389ms/step - loss: 3.1583 - val_loss: 2.5737
Epoch 26/500
6/6 [==============================] - 2s 396ms/step - loss: 3.1923 - val_loss: 2.4577
Epoch 27/500
6/6 [==============================] - 2s 396ms/step - loss: 3.2409 - val_loss: 2.3916
Epoch 28/500
6/6 [==============================] - 2s 383ms/step - loss: 3.4000 - val_loss: 2.3305
Epoch 29/500
6/6 [==============================] - 2s 380ms/step - loss: 3.2021 - val_loss: 2.3262
Epoch 30/500
6/6 [==============================] - 2s 393ms/step - loss: 3.0901 - val_loss: 2.3788
Epoch 31/500
6/6 [==============================] - 2s 392ms/step - loss: 3.1295 - val_loss: 2.3104
Epoch 32/500
6/6 [==============================] - 2s 383ms/step - loss: 3.0905 - val_loss: 2.2628
Epoch 33/500
6/6 [==============================] - 2s 396ms/step - loss: 3.2763 - val_loss: 2.2409
Epoch 34/500
6/6 [==============================] - 2s 393ms/step - loss: 3.1914 - val_loss: 2.3627
Epoch 35/500
6/6 [==============================] - 2s 402ms/step - loss: 3.0348 - val_loss: 2.3565
Epoch 36/500
6/6 [==============================] - 2s 399ms/step - loss: 2.7902 - val_loss: 2.4524
Epoch 37/500
6/6 [==============================] - 2s 394ms/step - loss: 2.7679 - val_loss: 2.3684
Epoch 38/500
6/6 [==============================] - 2s 399ms/step - loss: 3.1130 - val_loss: 2.2809
Epoch 39/500
6/6 [==============================] - 2s 393ms/step - loss: 2.8857 - val_loss: 2.2536
Epoch 40/500
6/6 [==============================] - 2s 392ms/step - loss: 3.0120 - val_loss: 2.3382
Epoch 41/500
6/6 [==============================] - 2s 397ms/step - loss: 3.0388 - val_loss: 2.3278
Epoch 42/500
6/6 [==============================] - 2s 392ms/step - loss: 2.7887 - val_loss: 2.3708
Epoch 43/500
6/6 [==============================] - 2s 387ms/step - loss: 2.8051 - val_loss: 2.3482
Epoch 44/500
6/6 [==============================] - 2s 387ms/step - loss: 2.9534 - val_loss: 2.3966
Epoch 45/500
6/6 [==============================] - 2s 390ms/step - loss: 3.0624 - val_loss: 2.3949
Epoch 46/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6904 - val_loss: 2.3622
Epoch 47/500
6/6 [==============================] - 2s 390ms/step - loss: 2.9035 - val_loss: 2.4180
Epoch 48/500
6/6 [==============================] - 2s 387ms/step - loss: 2.8621 - val_loss: 2.3639
Epoch 49/500
6/6 [==============================] - 2s 384ms/step - loss: 2.7218 - val_loss: 2.4201
Epoch 50/500
6/6 [==============================] - 2s 393ms/step - loss: 2.6997 - val_loss: 2.4420
Epoch 51/500
6/6 [==============================] - 3s 444ms/step - loss: 2.9826 - val_loss: 2.4018
Epoch 52/500
6/6 [==============================] - 3s 431ms/step - loss: 2.8377 - val_loss: 2.3450
Epoch 53/500
6/6 [==============================] - 3s 431ms/step - loss: 3.0024 - val_loss: 2.2434
Epoch 54/500
6/6 [==============================] - 2s 393ms/step - loss: 2.8939 - val_loss: 2.2470
Epoch 55/500
6/6 [==============================] - 2s 378ms/step - loss: 2.7371 - val_loss: 2.2506
Epoch 56/500
6/6 [==============================] - 3s 428ms/step - loss: 2.7787 - val_loss: 2.1975
Epoch 57/500
6/6 [==============================] - 3s 434ms/step - loss: 2.7833 - val_loss: 2.1537
Epoch 58/500
6/6 [==============================] - 3s 437ms/step - loss: 2.8349 - val_loss: 2.1524
Epoch 59/500
6/6 [==============================] - 3s 431ms/step - loss: 2.6466 - val_loss: 2.1217
Epoch 60/500
6/6 [==============================] - 2s 387ms/step - loss: 2.6005 - val_loss: 2.1479
Epoch 61/500
6/6 [==============================] - 2s 381ms/step - loss: 2.5787 - val_loss: 2.1470
Epoch 62/500
6/6 [==============================] - 2s 381ms/step - loss: 2.6510 - val_loss: 2.1762
Epoch 63/500
6/6 [==============================] - 2s 381ms/step - loss: 2.6063 - val_loss: 2.1714
Epoch 64/500
6/6 [==============================] - 2s 381ms/step - loss: 2.7198 - val_loss: 2.2020
Epoch 65/500
6/6 [==============================] - 2s 394ms/step - loss: 2.6713 - val_loss: 2.2369
Epoch 66/500
6/6 [==============================] - 2s 390ms/step - loss: 2.8485 - val_loss: 2.2189
Epoch 67/500
6/6 [==============================] - 2s 378ms/step - loss: 2.6506 - val_loss: 2.2745
Epoch 68/500
6/6 [==============================] - 2s 377ms/step - loss: 2.6134 - val_loss: 2.2886
Epoch 69/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6007 - val_loss: 2.2523
Epoch 70/500
6/6 [==============================] - 2s 378ms/step - loss: 2.6654 - val_loss: 2.2809
Epoch 71/500
6/6 [==============================] - 2s 387ms/step - loss: 2.6926 - val_loss: 2.2665
Epoch 72/500
6/6 [==============================] - 2s 377ms/step - loss: 2.6950 - val_loss: 2.2115
Epoch 73/500
6/6 [==============================] - 2s 387ms/step - loss: 2.7319 - val_loss: 2.2669
Epoch 74/500
6/6 [==============================] - 2s 384ms/step - loss: 2.6147 - val_loss: 2.2518
Epoch 75/500
6/6 [==============================] - 2s 396ms/step - loss: 2.3606 - val_loss: 2.1832
Epoch 76/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5246 - val_loss: 2.1681
Epoch 77/500
6/6 [==============================] - 3s 434ms/step - loss: 2.6858 - val_loss: 2.1140
Epoch 78/500
6/6 [==============================] - 3s 431ms/step - loss: 2.6297 - val_loss: 2.0958
Epoch 79/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5807 - val_loss: 2.1114
Epoch 80/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5261 - val_loss: 2.1257
Epoch 81/500
6/6 [==============================] - 2s 381ms/step - loss: 2.7245 - val_loss: 2.1315
Epoch 82/500
6/6 [==============================] - 2s 393ms/step - loss: 2.6563 - val_loss: 2.1112
Epoch 83/500
6/6 [==============================] - 2s 396ms/step - loss: 2.7431 - val_loss: 2.1007
Epoch 84/500
6/6 [==============================] - 2s 384ms/step - loss: 2.5371 - val_loss: 2.1045
Epoch 85/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5674 - val_loss: 2.0998
Epoch 86/500
6/6 [==============================] - 2s 396ms/step - loss: 2.5352 - val_loss: 2.1084
Epoch 87/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5727 - val_loss: 2.1568
Epoch 88/500
6/6 [==============================] - 2s 378ms/step - loss: 2.5478 - val_loss: 2.1259
Epoch 89/500
6/6 [==============================] - 2s 381ms/step - loss: 2.3984 - val_loss: 2.1613
Epoch 90/500
6/6 [==============================] - 2s 395ms/step - loss: 2.5827 - val_loss: 2.1299
Epoch 91/500
6/6 [==============================] - 3s 436ms/step - loss: 2.5428 - val_loss: 2.0806
Epoch 92/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5770 - val_loss: 2.1450
Epoch 93/500
6/6 [==============================] - 2s 388ms/step - loss: 2.5277 - val_loss: 2.1362
Epoch 94/500
6/6 [==============================] - 2s 365ms/step - loss: 2.6139 - val_loss: 2.1153
Epoch 95/500
6/6 [==============================] - 2s 382ms/step - loss: 2.4375 - val_loss: 2.1636
Epoch 96/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5295 - val_loss: 2.1292
Epoch 97/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5249 - val_loss: 2.1106
Epoch 98/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4492 - val_loss: 2.0899
Epoch 99/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5049 - val_loss: 2.1101
Epoch 100/500
6/6 [==============================] - 2s 374ms/step - loss: 2.4440 - val_loss: 2.1429
Epoch 101/500
6/6 [==============================] - 2s 387ms/step - loss: 2.7086 - val_loss: 2.1311
Epoch 102/500
6/6 [==============================] - 3s 434ms/step - loss: 2.4854 - val_loss: 2.0633
Epoch 103/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6748 - val_loss: 2.1034
Epoch 104/500
6/6 [==============================] - 2s 384ms/step - loss: 2.5733 - val_loss: 2.1872
Epoch 105/500
6/6 [==============================] - 2s 393ms/step - loss: 2.3211 - val_loss: 2.1534
Epoch 106/500
6/6 [==============================] - 2s 387ms/step - loss: 2.4706 - val_loss: 2.1779
Epoch 107/500
6/6 [==============================] - 2s 396ms/step - loss: 2.4624 - val_loss: 2.1439
Epoch 108/500
6/6 [==============================] - 2s 393ms/step - loss: 2.3845 - val_loss: 2.1373
Epoch 109/500
6/6 [==============================] - 2s 379ms/step - loss: 2.3696 - val_loss: 2.0812
Epoch 110/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5053 - val_loss: 2.0856
Epoch 111/500
6/6 [==============================] - 2s 380ms/step - loss: 2.8007 - val_loss: 2.0889
Epoch 112/500
6/6 [==============================] - 2s 387ms/step - loss: 2.3327 - val_loss: 2.1420
Epoch 113/500
6/6 [==============================] - 2s 389ms/step - loss: 2.7612 - val_loss: 2.1469
Epoch 114/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5716 - val_loss: 2.0890
Epoch 115/500
6/6 [==============================] - 3s 443ms/step - loss: 2.5826 - val_loss: 2.0522
Epoch 116/500
6/6 [==============================] - 2s 428ms/step - loss: 2.3962 - val_loss: 1.9936
Epoch 117/500
6/6 [==============================] - 2s 422ms/step - loss: 2.5514 - val_loss: 1.9639
Epoch 118/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4878 - val_loss: 2.0073
Epoch 119/500
6/6 [==============================] - 2s 381ms/step - loss: 2.4536 - val_loss: 2.0303
Epoch 120/500
6/6 [==============================] - 2s 391ms/step - loss: 2.6124 - val_loss: 2.0223
Epoch 121/500
6/6 [==============================] - 2s 390ms/step - loss: 2.2475 - val_loss: 1.9900
Epoch 122/500
6/6 [==============================] - 2s 393ms/step - loss: 2.4150 - val_loss: 1.9942
Epoch 123/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4079 - val_loss: 2.0106
Epoch 124/500
6/6 [==============================] - 2s 393ms/step - loss: 2.3626 - val_loss: 2.0216
Epoch 125/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4178 - val_loss: 2.0346
Epoch 126/500
6/6 [==============================] - 2s 396ms/step - loss: 2.3546 - val_loss: 2.0601
Epoch 127/500
6/6 [==============================] - 2s 393ms/step - loss: 2.6445 - val_loss: 2.0644
Epoch 128/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5490 - val_loss: 2.0023
Epoch 129/500
6/6 [==============================] - 2s 390ms/step - loss: 2.3887 - val_loss: 1.9844
Epoch 130/500
6/6 [==============================] - 2s 393ms/step - loss: 2.3026 - val_loss: 2.0134
Epoch 131/500
6/6 [==============================] - 2s 384ms/step - loss: 2.4070 - val_loss: 1.9947
Epoch 132/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5432 - val_loss: 2.0242
Epoch 133/500
6/6 [==============================] - 2s 401ms/step - loss: 2.4570 - val_loss: 2.0292
Epoch 134/500
6/6 [==============================] - 2s 384ms/step - loss: 2.5328 - val_loss: 2.0208
Epoch 135/500
6/6 [==============================] - 2s 396ms/step - loss: 2.4598 - val_loss: 2.0307
Epoch 136/500
6/6 [==============================] - 2s 396ms/step - loss: 2.3794 - val_loss: 1.9901
Epoch 137/500
6/6 [==============================] - 3s 557ms/step - loss: 2.2766 - val_loss: 1.9669
Epoch 137: early stopping
Epoch 1/500
6/6 [==============================] - 36s 794ms/step - loss: 3.7758 - val_loss: 2.0381
Epoch 2/500
6/6 [==============================] - 3s 478ms/step - loss: 2.6946 - val_loss: 2.2042
Epoch 3/500
6/6 [==============================] - 3s 487ms/step - loss: 2.9776 - val_loss: 2.2287
Epoch 4/500
6/6 [==============================] - 3s 478ms/step - loss: 2.5856 - val_loss: 2.2684
Epoch 5/500
6/6 [==============================] - 3s 512ms/step - loss: 2.3664 - val_loss: 2.2993
Epoch 6/500
6/6 [==============================] - 3s 509ms/step - loss: 2.3887 - val_loss: 2.2310
Epoch 7/500
6/6 [==============================] - 3s 498ms/step - loss: 2.2560 - val_loss: 2.1980
Epoch 8/500
6/6 [==============================] - 3s 440ms/step - loss: 2.0344 - val_loss: 2.2257
Epoch 9/500
6/6 [==============================] - 3s 443ms/step - loss: 2.2020 - val_loss: 2.2778
Epoch 10/500
6/6 [==============================] - 3s 468ms/step - loss: 1.9341 - val_loss: 2.2924
Epoch 11/500
6/6 [==============================] - 3s 468ms/step - loss: 2.0502 - val_loss: 2.4367
Epoch 12/500
6/6 [==============================] - 3s 484ms/step - loss: 1.8696 - val_loss: 2.3222
Epoch 13/500
6/6 [==============================] - 3s 480ms/step - loss: 1.9263 - val_loss: 2.2169
Epoch 14/500
6/6 [==============================] - 3s 533ms/step - loss: 1.8333 - val_loss: 2.1708
Epoch 15/500
6/6 [==============================] - 3s 546ms/step - loss: 2.0518 - val_loss: 2.0981
Epoch 16/500
6/6 [==============================] - 3s 530ms/step - loss: 1.8183 - val_loss: 1.9859
Epoch 17/500
6/6 [==============================] - 3s 519ms/step - loss: 1.8055 - val_loss: 1.9726
Epoch 18/500
6/6 [==============================] - 3s 525ms/step - loss: 1.7149 - val_loss: 1.9018
Epoch 19/500
6/6 [==============================] - 3s 480ms/step - loss: 1.6949 - val_loss: 1.9105
Epoch 20/500
6/6 [==============================] - 3s 475ms/step - loss: 1.6463 - val_loss: 1.9335
Epoch 21/500
6/6 [==============================] - 3s 494ms/step - loss: 1.7314 - val_loss: 1.9094
Epoch 22/500
6/6 [==============================] - 3s 476ms/step - loss: 1.8258 - val_loss: 1.9169
Epoch 23/500
6/6 [==============================] - 3s 539ms/step - loss: 1.6903 - val_loss: 1.8601
Epoch 24/500
6/6 [==============================] - 3s 488ms/step - loss: 1.7478 - val_loss: 1.9682
Epoch 25/500
6/6 [==============================] - 3s 487ms/step - loss: 1.6629 - val_loss: 1.8949
Epoch 26/500
6/6 [==============================] - 3s 483ms/step - loss: 1.6378 - val_loss: 1.9156
Epoch 27/500
6/6 [==============================] - 3s 501ms/step - loss: 1.6889 - val_loss: 1.8949
Epoch 28/500
6/6 [==============================] - 3s 485ms/step - loss: 1.5451 - val_loss: 1.8723
Epoch 29/500
6/6 [==============================] - 3s 489ms/step - loss: 1.5546 - val_loss: 1.8789
Epoch 30/500
6/6 [==============================] - 3s 536ms/step - loss: 1.5733 - val_loss: 1.8569
Epoch 31/500
6/6 [==============================] - 3s 553ms/step - loss: 1.5648 - val_loss: 1.8011
Epoch 32/500
6/6 [==============================] - 3s 538ms/step - loss: 1.5778 - val_loss: 1.7997
Epoch 33/500
6/6 [==============================] - 3s 512ms/step - loss: 1.5612 - val_loss: 1.7660
Epoch 34/500
6/6 [==============================] - 3s 524ms/step - loss: 1.5079 - val_loss: 1.7483
Epoch 35/500
6/6 [==============================] - 3s 543ms/step - loss: 1.6212 - val_loss: 1.7460
Epoch 36/500
6/6 [==============================] - 3s 519ms/step - loss: 1.5899 - val_loss: 1.6714
Epoch 37/500
6/6 [==============================] - 3s 543ms/step - loss: 1.4707 - val_loss: 1.6638
Epoch 38/500
6/6 [==============================] - 3s 518ms/step - loss: 1.4946 - val_loss: 1.6259
Epoch 39/500
6/6 [==============================] - 3s 474ms/step - loss: 1.6271 - val_loss: 1.6733
Epoch 40/500
6/6 [==============================] - 3s 474ms/step - loss: 1.4780 - val_loss: 1.6553
Epoch 41/500
6/6 [==============================] - 3s 488ms/step - loss: 1.4669 - val_loss: 1.6306
Epoch 42/500
6/6 [==============================] - 3s 494ms/step - loss: 1.4722 - val_loss: 1.6331
Epoch 43/500
6/6 [==============================] - 3s 544ms/step - loss: 1.4536 - val_loss: 1.6010
Epoch 44/500
6/6 [==============================] - 3s 540ms/step - loss: 1.3891 - val_loss: 1.5875
Epoch 45/500
6/6 [==============================] - 3s 483ms/step - loss: 1.6030 - val_loss: 1.6126
Epoch 46/500
6/6 [==============================] - 3s 500ms/step - loss: 1.4820 - val_loss: 1.5993
Epoch 47/500
6/6 [==============================] - 3s 487ms/step - loss: 1.4444 - val_loss: 1.5890
Epoch 48/500
6/6 [==============================] - 3s 552ms/step - loss: 1.6512 - val_loss: 1.5859
Epoch 49/500
6/6 [==============================] - 3s 505ms/step - loss: 1.5215 - val_loss: 1.6182
Epoch 50/500
6/6 [==============================] - 3s 548ms/step - loss: 1.6015 - val_loss: 1.5763
Epoch 51/500
6/6 [==============================] - 3s 526ms/step - loss: 1.5502 - val_loss: 1.5687
Epoch 52/500
6/6 [==============================] - 3s 544ms/step - loss: 1.3895 - val_loss: 1.5651
Epoch 53/500
6/6 [==============================] - 3s 499ms/step - loss: 1.5089 - val_loss: 1.5754
Epoch 54/500
6/6 [==============================] - 3s 502ms/step - loss: 1.5773 - val_loss: 1.5709
Epoch 55/500
6/6 [==============================] - 3s 534ms/step - loss: 1.5857 - val_loss: 1.5636
Epoch 56/500
6/6 [==============================] - 3s 475ms/step - loss: 1.5428 - val_loss: 1.5677
Epoch 57/500
6/6 [==============================] - 3s 525ms/step - loss: 1.5010 - val_loss: 1.5431
Epoch 58/500
6/6 [==============================] - 3s 478ms/step - loss: 1.4304 - val_loss: 1.5518
Epoch 59/500
6/6 [==============================] - 3s 502ms/step - loss: 1.4030 - val_loss: 1.5623
Epoch 60/500
6/6 [==============================] - 3s 486ms/step - loss: 1.4971 - val_loss: 1.5535
Epoch 61/500
6/6 [==============================] - 3s 550ms/step - loss: 1.3990 - val_loss: 1.5055
Epoch 62/500
6/6 [==============================] - 3s 512ms/step - loss: 1.3729 - val_loss: 1.5157
Epoch 63/500
6/6 [==============================] - 3s 464ms/step - loss: 1.2844 - val_loss: 1.5281
Epoch 64/500
6/6 [==============================] - 3s 500ms/step - loss: 1.4166 - val_loss: 1.5208
Epoch 65/500
6/6 [==============================] - 3s 538ms/step - loss: 1.4495 - val_loss: 1.5012
Epoch 66/500
6/6 [==============================] - 3s 493ms/step - loss: 1.3945 - val_loss: 1.5409
Epoch 67/500
6/6 [==============================] - 3s 493ms/step - loss: 1.4228 - val_loss: 1.5267
Epoch 68/500
6/6 [==============================] - 3s 509ms/step - loss: 1.3202 - val_loss: 1.5184
Epoch 69/500
6/6 [==============================] - 3s 534ms/step - loss: 1.4070 - val_loss: 1.4974
Epoch 70/500
6/6 [==============================] - 3s 496ms/step - loss: 1.5098 - val_loss: 1.5067
Epoch 71/500
6/6 [==============================] - 4s 562ms/step - loss: 1.4041 - val_loss: 1.4864
Epoch 72/500
6/6 [==============================] - 3s 521ms/step - loss: 1.2750 - val_loss: 1.4591
Epoch 73/500
6/6 [==============================] - 3s 502ms/step - loss: 1.3452 - val_loss: 1.4797
Epoch 74/500
6/6 [==============================] - 3s 530ms/step - loss: 1.3603 - val_loss: 1.4826
Epoch 75/500
6/6 [==============================] - 3s 490ms/step - loss: 1.3005 - val_loss: 1.5127
Epoch 76/500
6/6 [==============================] - 3s 482ms/step - loss: 1.4115 - val_loss: 1.5001
Epoch 77/500
6/6 [==============================] - 3s 524ms/step - loss: 1.2510 - val_loss: 1.5146
Epoch 78/500
6/6 [==============================] - 3s 493ms/step - loss: 1.4252 - val_loss: 1.4945
Epoch 79/500
6/6 [==============================] - 3s 512ms/step - loss: 1.4388 - val_loss: 1.5249
Epoch 80/500
6/6 [==============================] - 3s 511ms/step - loss: 1.3810 - val_loss: 1.5411
Epoch 81/500
6/6 [==============================] - 3s 507ms/step - loss: 1.3759 - val_loss: 1.5804
Epoch 82/500
6/6 [==============================] - 3s 491ms/step - loss: 1.3483 - val_loss: 1.5217
Epoch 83/500
6/6 [==============================] - 3s 493ms/step - loss: 1.3665 - val_loss: 1.5003
Epoch 84/500
6/6 [==============================] - 3s 505ms/step - loss: 1.3819 - val_loss: 1.4995
Epoch 85/500
6/6 [==============================] - 3s 515ms/step - loss: 1.3996 - val_loss: 1.4906
Epoch 86/500
6/6 [==============================] - 3s 518ms/step - loss: 1.2877 - val_loss: 1.4882
Epoch 87/500
6/6 [==============================] - 3s 510ms/step - loss: 1.4552 - val_loss: 1.4936
Epoch 88/500
6/6 [==============================] - 3s 508ms/step - loss: 1.3554 - val_loss: 1.5287
Epoch 89/500
6/6 [==============================] - 3s 507ms/step - loss: 1.4064 - val_loss: 1.5475
Epoch 90/500
6/6 [==============================] - 3s 497ms/step - loss: 1.4680 - val_loss: 1.5897
Epoch 91/500
6/6 [==============================] - 3s 510ms/step - loss: 1.2822 - val_loss: 1.5601
Epoch 92/500
6/6 [==============================] - 5s 804ms/step - loss: 1.3016 - val_loss: 1.5489
Epoch 92: early stopping
Epoch 1/500
6/6 [==============================] - 11s 712ms/step - loss: 10.0477 - val_loss: 7.5509
Epoch 2/500
6/6 [==============================] - 2s 386ms/step - loss: 8.2879 - val_loss: 5.6979
Epoch 3/500
6/6 [==============================] - 2s 396ms/step - loss: 6.5195 - val_loss: 5.2998
Epoch 4/500
6/6 [==============================] - 2s 386ms/step - loss: 6.0024 - val_loss: 6.0061
Epoch 5/500
6/6 [==============================] - 2s 396ms/step - loss: 5.3492 - val_loss: 4.2051
Epoch 6/500
6/6 [==============================] - 2s 380ms/step - loss: 5.1021 - val_loss: 3.8770
Epoch 7/500
6/6 [==============================] - 2s 389ms/step - loss: 4.8440 - val_loss: 4.9100
Epoch 8/500
6/6 [==============================] - 2s 383ms/step - loss: 4.7188 - val_loss: 4.4067
Epoch 9/500
6/6 [==============================] - 2s 393ms/step - loss: 4.3833 - val_loss: 4.3284
Epoch 10/500
6/6 [==============================] - 2s 393ms/step - loss: 4.3599 - val_loss: 5.1191
Epoch 11/500
6/6 [==============================] - 2s 402ms/step - loss: 4.1813 - val_loss: 5.2437
Epoch 12/500
6/6 [==============================] - 2s 408ms/step - loss: 3.9407 - val_loss: 4.6951
Epoch 13/500
6/6 [==============================] - 2s 402ms/step - loss: 3.9699 - val_loss: 4.1362
Epoch 14/500
6/6 [==============================] - 2s 389ms/step - loss: 3.8820 - val_loss: 3.7771
Epoch 15/500
6/6 [==============================] - 2s 377ms/step - loss: 3.6622 - val_loss: 3.7047
Epoch 16/500
6/6 [==============================] - 2s 396ms/step - loss: 3.7941 - val_loss: 3.1643
Epoch 17/500
6/6 [==============================] - 2s 396ms/step - loss: 3.6589 - val_loss: 3.2470
Epoch 18/500
6/6 [==============================] - 2s 398ms/step - loss: 3.3801 - val_loss: 3.2373
Epoch 19/500
6/6 [==============================] - 2s 399ms/step - loss: 3.7515 - val_loss: 3.1438
Epoch 20/500
6/6 [==============================] - 2s 399ms/step - loss: 3.6041 - val_loss: 2.7488
Epoch 21/500
6/6 [==============================] - 2s 389ms/step - loss: 3.3796 - val_loss: 2.7777
Epoch 22/500
6/6 [==============================] - 2s 402ms/step - loss: 3.2608 - val_loss: 2.6185
Epoch 23/500
6/6 [==============================] - 2s 393ms/step - loss: 3.3750 - val_loss: 2.5087
Epoch 24/500
6/6 [==============================] - 2s 389ms/step - loss: 3.4451 - val_loss: 2.6195
Epoch 25/500
6/6 [==============================] - 2s 386ms/step - loss: 3.2088 - val_loss: 2.7302
Epoch 26/500
6/6 [==============================] - 2s 408ms/step - loss: 3.2482 - val_loss: 2.7482
Epoch 27/500
6/6 [==============================] - 2s 396ms/step - loss: 3.1904 - val_loss: 2.6776
Epoch 28/500
6/6 [==============================] - 2s 389ms/step - loss: 3.0646 - val_loss: 2.8588
Epoch 29/500
6/6 [==============================] - 2s 393ms/step - loss: 3.2350 - val_loss: 2.5952
Epoch 30/500
6/6 [==============================] - 2s 396ms/step - loss: 3.1500 - val_loss: 2.5042
Epoch 31/500
6/6 [==============================] - 2s 392ms/step - loss: 3.2580 - val_loss: 2.4085
Epoch 32/500
6/6 [==============================] - 2s 392ms/step - loss: 3.1242 - val_loss: 2.3242
Epoch 33/500
6/6 [==============================] - 2s 389ms/step - loss: 3.0390 - val_loss: 2.2892
Epoch 34/500
6/6 [==============================] - 2s 391ms/step - loss: 2.9448 - val_loss: 2.2810
Epoch 35/500
6/6 [==============================] - 2s 393ms/step - loss: 3.1135 - val_loss: 2.3103
Epoch 36/500
6/6 [==============================] - 2s 393ms/step - loss: 2.9659 - val_loss: 2.2373
Epoch 37/500
6/6 [==============================] - 2s 408ms/step - loss: 3.0920 - val_loss: 2.2098
Epoch 38/500
6/6 [==============================] - 2s 392ms/step - loss: 2.8670 - val_loss: 2.2754
Epoch 39/500
6/6 [==============================] - 2s 396ms/step - loss: 2.7138 - val_loss: 2.2634
Epoch 40/500
6/6 [==============================] - 2s 370ms/step - loss: 2.6739 - val_loss: 2.2239
Epoch 41/500
6/6 [==============================] - 2s 393ms/step - loss: 2.8227 - val_loss: 2.1927
Epoch 42/500
6/6 [==============================] - 2s 400ms/step - loss: 2.8120 - val_loss: 2.2662
Epoch 43/500
6/6 [==============================] - 2s 397ms/step - loss: 2.9857 - val_loss: 2.2191
Epoch 44/500
6/6 [==============================] - 2s 396ms/step - loss: 2.8995 - val_loss: 2.2607
Epoch 45/500
6/6 [==============================] - 2s 383ms/step - loss: 2.8052 - val_loss: 2.3164
Epoch 46/500
6/6 [==============================] - 2s 399ms/step - loss: 2.7765 - val_loss: 2.3671
Epoch 47/500
6/6 [==============================] - 2s 396ms/step - loss: 2.8666 - val_loss: 2.3797
Epoch 48/500
6/6 [==============================] - 2s 396ms/step - loss: 2.9126 - val_loss: 2.2941
Epoch 49/500
6/6 [==============================] - 2s 399ms/step - loss: 2.7187 - val_loss: 2.2603
Epoch 50/500
6/6 [==============================] - 2s 377ms/step - loss: 2.9033 - val_loss: 2.2857
Epoch 51/500
6/6 [==============================] - 3s 443ms/step - loss: 2.8564 - val_loss: 2.3785
Epoch 52/500
6/6 [==============================] - 3s 435ms/step - loss: 2.9156 - val_loss: 2.2860
Epoch 53/500
6/6 [==============================] - 2s 392ms/step - loss: 2.9522 - val_loss: 2.3129
Epoch 54/500
6/6 [==============================] - 2s 385ms/step - loss: 2.6845 - val_loss: 2.2868
Epoch 55/500
6/6 [==============================] - 2s 383ms/step - loss: 2.7484 - val_loss: 2.3059
Epoch 56/500
6/6 [==============================] - 3s 418ms/step - loss: 2.6791 - val_loss: 2.2693
Epoch 57/500
6/6 [==============================] - 3s 427ms/step - loss: 2.7576 - val_loss: 2.2619
Epoch 58/500
6/6 [==============================] - 3s 436ms/step - loss: 2.8578 - val_loss: 2.1910
Epoch 59/500
6/6 [==============================] - 3s 431ms/step - loss: 2.7348 - val_loss: 2.1799
Epoch 60/500
6/6 [==============================] - 2s 395ms/step - loss: 2.5678 - val_loss: 2.1983
Epoch 61/500
6/6 [==============================] - 2s 380ms/step - loss: 2.6924 - val_loss: 2.2903
Epoch 62/500
6/6 [==============================] - 2s 389ms/step - loss: 2.7401 - val_loss: 2.2080
Epoch 63/500
6/6 [==============================] - 3s 424ms/step - loss: 2.7186 - val_loss: 2.1107
Epoch 64/500
6/6 [==============================] - 3s 424ms/step - loss: 2.5951 - val_loss: 2.0910
Epoch 65/500
6/6 [==============================] - 3s 427ms/step - loss: 2.7563 - val_loss: 2.0744
Epoch 66/500
6/6 [==============================] - 2s 392ms/step - loss: 2.6029 - val_loss: 2.0822
Epoch 67/500
6/6 [==============================] - 2s 386ms/step - loss: 2.5192 - val_loss: 2.1163
Epoch 68/500
6/6 [==============================] - 2s 389ms/step - loss: 2.7739 - val_loss: 2.0960
Epoch 69/500
6/6 [==============================] - 2s 399ms/step - loss: 2.6959 - val_loss: 2.1288
Epoch 70/500
6/6 [==============================] - 2s 389ms/step - loss: 2.6302 - val_loss: 2.1225
Epoch 71/500
6/6 [==============================] - 2s 396ms/step - loss: 2.7020 - val_loss: 2.1302
Epoch 72/500
6/6 [==============================] - 2s 386ms/step - loss: 2.8176 - val_loss: 2.1249
Epoch 73/500
6/6 [==============================] - 2s 392ms/step - loss: 2.7104 - val_loss: 2.1066
Epoch 74/500
6/6 [==============================] - 2s 392ms/step - loss: 2.7205 - val_loss: 2.1030
Epoch 75/500
6/6 [==============================] - 3s 430ms/step - loss: 2.7895 - val_loss: 2.0436
Epoch 76/500
6/6 [==============================] - 2s 386ms/step - loss: 2.6896 - val_loss: 2.0571
Epoch 77/500
6/6 [==============================] - 2s 380ms/step - loss: 2.4981 - val_loss: 2.0850
Epoch 78/500
6/6 [==============================] - 2s 386ms/step - loss: 2.6381 - val_loss: 2.0687
Epoch 79/500
6/6 [==============================] - 2s 393ms/step - loss: 2.6188 - val_loss: 2.0692
Epoch 80/500
6/6 [==============================] - 2s 392ms/step - loss: 2.4677 - val_loss: 2.1365
Epoch 81/500
6/6 [==============================] - 2s 383ms/step - loss: 2.5869 - val_loss: 2.1104
Epoch 82/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6138 - val_loss: 2.1069
Epoch 83/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5642 - val_loss: 2.1281
Epoch 84/500
6/6 [==============================] - 2s 389ms/step - loss: 2.6717 - val_loss: 2.1121
Epoch 85/500
6/6 [==============================] - 2s 380ms/step - loss: 2.4724 - val_loss: 2.1329
Epoch 86/500
6/6 [==============================] - 2s 386ms/step - loss: 2.4400 - val_loss: 2.1006
Epoch 87/500
6/6 [==============================] - 2s 402ms/step - loss: 2.5679 - val_loss: 2.0739
Epoch 88/500
6/6 [==============================] - 2s 386ms/step - loss: 2.6060 - val_loss: 2.0568
Epoch 89/500
6/6 [==============================] - 2s 386ms/step - loss: 2.4450 - val_loss: 2.0788
Epoch 90/500
6/6 [==============================] - 2s 397ms/step - loss: 2.6909 - val_loss: 2.1022
Epoch 91/500
6/6 [==============================] - 2s 385ms/step - loss: 2.6769 - val_loss: 2.1089
Epoch 92/500
6/6 [==============================] - 2s 395ms/step - loss: 2.4337 - val_loss: 2.0770
Epoch 93/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4211 - val_loss: 2.1002
Epoch 94/500
6/6 [==============================] - 2s 393ms/step - loss: 2.6193 - val_loss: 2.0932
Epoch 95/500
6/6 [==============================] - 3s 547ms/step - loss: 2.5044 - val_loss: 2.0716
Epoch 95: early stopping
Epoch 1/500
6/6 [==============================] - 36s 838ms/step - loss: 3.9367 - val_loss: 2.0699
Epoch 2/500
6/6 [==============================] - 3s 461ms/step - loss: 3.0527 - val_loss: 2.1793
Epoch 3/500
6/6 [==============================] - 3s 466ms/step - loss: 2.7435 - val_loss: 2.2799
Epoch 4/500
6/6 [==============================] - 3s 446ms/step - loss: 2.5567 - val_loss: 2.3656
Epoch 5/500
6/6 [==============================] - 3s 443ms/step - loss: 2.4121 - val_loss: 2.4838
Epoch 6/500
6/6 [==============================] - 3s 483ms/step - loss: 2.2819 - val_loss: 2.3257
Epoch 7/500
6/6 [==============================] - 3s 493ms/step - loss: 2.0961 - val_loss: 2.1665
Epoch 8/500
6/6 [==============================] - 3s 496ms/step - loss: 2.1911 - val_loss: 2.1444
Epoch 9/500
6/6 [==============================] - 3s 531ms/step - loss: 2.1374 - val_loss: 2.1381
Epoch 10/500
6/6 [==============================] - 3s 505ms/step - loss: 2.1643 - val_loss: 2.1180
Epoch 11/500
6/6 [==============================] - 3s 520ms/step - loss: 1.9384 - val_loss: 2.1134
Epoch 12/500
6/6 [==============================] - 3s 518ms/step - loss: 1.9106 - val_loss: 2.0962
Epoch 13/500
6/6 [==============================] - 3s 489ms/step - loss: 1.9528 - val_loss: 2.1159
Epoch 14/500
6/6 [==============================] - 3s 527ms/step - loss: 1.8451 - val_loss: 2.0871
Epoch 15/500
6/6 [==============================] - 3s 503ms/step - loss: 1.8724 - val_loss: 1.9864
Epoch 16/500
6/6 [==============================] - 3s 527ms/step - loss: 1.7970 - val_loss: 1.9801
Epoch 17/500
6/6 [==============================] - 3s 544ms/step - loss: 1.7567 - val_loss: 1.8975
Epoch 18/500
6/6 [==============================] - 3s 542ms/step - loss: 1.8428 - val_loss: 1.8330
Epoch 19/500
6/6 [==============================] - 3s 535ms/step - loss: 1.9030 - val_loss: 1.7957
Epoch 20/500
6/6 [==============================] - 3s 521ms/step - loss: 1.7693 - val_loss: 1.7739
Epoch 21/500
6/6 [==============================] - 3s 496ms/step - loss: 1.8283 - val_loss: 1.7761
Epoch 22/500
6/6 [==============================] - 3s 516ms/step - loss: 1.7108 - val_loss: 1.7669
Epoch 23/500
6/6 [==============================] - 3s 490ms/step - loss: 1.8159 - val_loss: 1.7726
Epoch 24/500
6/6 [==============================] - 3s 477ms/step - loss: 1.7628 - val_loss: 1.7955
Epoch 25/500
6/6 [==============================] - 3s 501ms/step - loss: 1.6740 - val_loss: 1.7970
Epoch 26/500
6/6 [==============================] - 3s 477ms/step - loss: 1.6933 - val_loss: 1.7878
Epoch 27/500
6/6 [==============================] - 3s 543ms/step - loss: 1.7560 - val_loss: 1.7649
Epoch 28/500
6/6 [==============================] - 3s 524ms/step - loss: 1.6816 - val_loss: 1.7628
Epoch 29/500
6/6 [==============================] - 3s 526ms/step - loss: 1.6558 - val_loss: 1.7490
Epoch 30/500
6/6 [==============================] - 3s 482ms/step - loss: 1.6242 - val_loss: 1.7571
Epoch 31/500
6/6 [==============================] - 3s 540ms/step - loss: 1.7018 - val_loss: 1.7296
Epoch 32/500
6/6 [==============================] - 3s 528ms/step - loss: 1.6016 - val_loss: 1.6976
Epoch 33/500
6/6 [==============================] - 3s 486ms/step - loss: 1.6160 - val_loss: 1.7107
Epoch 34/500
6/6 [==============================] - 3s 490ms/step - loss: 1.6550 - val_loss: 1.7005
Epoch 35/500
6/6 [==============================] - 3s 540ms/step - loss: 1.5285 - val_loss: 1.6634
Epoch 36/500
6/6 [==============================] - 3s 550ms/step - loss: 1.6605 - val_loss: 1.6610
Epoch 37/500
6/6 [==============================] - 3s 529ms/step - loss: 1.5815 - val_loss: 1.6510
Epoch 38/500
6/6 [==============================] - 3s 493ms/step - loss: 1.5672 - val_loss: 1.6724
Epoch 39/500
6/6 [==============================] - 3s 532ms/step - loss: 1.5235 - val_loss: 1.6393
Epoch 40/500
6/6 [==============================] - 3s 533ms/step - loss: 1.4677 - val_loss: 1.6130
Epoch 41/500
6/6 [==============================] - 3s 514ms/step - loss: 1.5007 - val_loss: 1.6018
Epoch 42/500
6/6 [==============================] - 4s 528ms/step - loss: 1.5986 - val_loss: 1.5807
Epoch 43/500
6/6 [==============================] - 3s 534ms/step - loss: 1.5922 - val_loss: 1.5669
Epoch 44/500
6/6 [==============================] - 3s 537ms/step - loss: 1.5486 - val_loss: 1.5609
Epoch 45/500
6/6 [==============================] - 3s 530ms/step - loss: 1.4513 - val_loss: 1.4929
Epoch 46/500
6/6 [==============================] - 3s 496ms/step - loss: 1.7033 - val_loss: 1.5115
Epoch 47/500
6/6 [==============================] - 3s 479ms/step - loss: 1.3835 - val_loss: 1.5191
Epoch 48/500
6/6 [==============================] - 3s 556ms/step - loss: 1.4667 - val_loss: 1.4765
Epoch 49/500
6/6 [==============================] - 3s 484ms/step - loss: 1.6052 - val_loss: 1.4945
Epoch 50/500
6/6 [==============================] - 3s 504ms/step - loss: 1.5655 - val_loss: 1.4872
Epoch 51/500
6/6 [==============================] - 3s 497ms/step - loss: 1.4514 - val_loss: 1.4796
Epoch 52/500
6/6 [==============================] - 3s 507ms/step - loss: 1.4255 - val_loss: 1.4907
Epoch 53/500
6/6 [==============================] - 3s 505ms/step - loss: 1.5550 - val_loss: 1.5042
Epoch 54/500
6/6 [==============================] - 3s 498ms/step - loss: 1.5054 - val_loss: 1.5359
Epoch 55/500
6/6 [==============================] - 3s 491ms/step - loss: 1.5431 - val_loss: 1.5269
Epoch 56/500
6/6 [==============================] - 3s 501ms/step - loss: 1.3757 - val_loss: 1.4920
Epoch 57/500
6/6 [==============================] - 3s 496ms/step - loss: 1.2864 - val_loss: 1.4820
Epoch 58/500
6/6 [==============================] - 3s 524ms/step - loss: 1.5402 - val_loss: 1.4569
Epoch 59/500
6/6 [==============================] - 3s 506ms/step - loss: 1.5139 - val_loss: 1.4791
Epoch 60/500
6/6 [==============================] - 3s 551ms/step - loss: 1.4894 - val_loss: 1.4548
Epoch 61/500
6/6 [==============================] - 3s 499ms/step - loss: 1.3184 - val_loss: 1.4655
Epoch 62/500
6/6 [==============================] - 3s 482ms/step - loss: 1.4942 - val_loss: 1.4817
Epoch 63/500
6/6 [==============================] - 3s 502ms/step - loss: 1.5217 - val_loss: 1.5065
Epoch 64/500
6/6 [==============================] - 3s 510ms/step - loss: 1.4089 - val_loss: 1.5799
Epoch 65/500
6/6 [==============================] - 3s 500ms/step - loss: 1.4981 - val_loss: 1.5464
Epoch 66/500
6/6 [==============================] - 3s 511ms/step - loss: 1.3563 - val_loss: 1.5313
Epoch 67/500
6/6 [==============================] - 3s 516ms/step - loss: 1.3863 - val_loss: 1.5373
Epoch 68/500
6/6 [==============================] - 3s 516ms/step - loss: 1.5091 - val_loss: 1.5789
Epoch 69/500
6/6 [==============================] - 3s 504ms/step - loss: 1.6179 - val_loss: 1.5719
Epoch 70/500
6/6 [==============================] - 3s 511ms/step - loss: 1.5603 - val_loss: 1.5474
Epoch 71/500
6/6 [==============================] - 3s 494ms/step - loss: 1.4891 - val_loss: 1.5571
Epoch 72/500
6/6 [==============================] - 3s 508ms/step - loss: 1.3852 - val_loss: 1.5093
Epoch 73/500
6/6 [==============================] - 3s 516ms/step - loss: 1.3658 - val_loss: 1.5576
Epoch 74/500
6/6 [==============================] - 3s 497ms/step - loss: 1.4494 - val_loss: 1.4990
Epoch 75/500
6/6 [==============================] - 3s 518ms/step - loss: 1.5400 - val_loss: 1.5288
Epoch 76/500
6/6 [==============================] - 3s 502ms/step - loss: 1.5186 - val_loss: 1.5785
Epoch 77/500
6/6 [==============================] - 3s 508ms/step - loss: 1.3590 - val_loss: 1.6139
Epoch 78/500
6/6 [==============================] - 3s 503ms/step - loss: 1.3251 - val_loss: 1.5744
Epoch 79/500
6/6 [==============================] - 3s 508ms/step - loss: 1.3400 - val_loss: 1.5843
Epoch 80/500
6/6 [==============================] - 5s 811ms/step - loss: 1.4332 - val_loss: 1.5552
Epoch 80: early stopping
Epoch 1/500
6/6 [==============================] - 11s 714ms/step - loss: 9.7899 - val_loss: 7.4863
Epoch 2/500
6/6 [==============================] - 2s 391ms/step - loss: 8.2214 - val_loss: 8.3225
Epoch 3/500
6/6 [==============================] - 2s 391ms/step - loss: 6.8674 - val_loss: 6.9788
Epoch 4/500
6/6 [==============================] - 2s 386ms/step - loss: 5.8535 - val_loss: 8.7487
Epoch 5/500
6/6 [==============================] - 2s 392ms/step - loss: 5.5300 - val_loss: 9.5133
Epoch 6/500
6/6 [==============================] - 2s 377ms/step - loss: 5.4593 - val_loss: 6.5243
Epoch 7/500
6/6 [==============================] - 2s 395ms/step - loss: 4.8722 - val_loss: 5.9199
Epoch 8/500
6/6 [==============================] - 2s 399ms/step - loss: 4.1851 - val_loss: 5.0454
Epoch 9/500
6/6 [==============================] - 2s 384ms/step - loss: 4.6981 - val_loss: 5.5525
Epoch 10/500
6/6 [==============================] - 2s 380ms/step - loss: 4.3173 - val_loss: 6.1439
Epoch 11/500
6/6 [==============================] - 2s 395ms/step - loss: 4.2660 - val_loss: 6.3558
Epoch 12/500
6/6 [==============================] - 2s 407ms/step - loss: 4.2571 - val_loss: 5.7580
Epoch 13/500
6/6 [==============================] - 2s 396ms/step - loss: 4.0326 - val_loss: 5.4164
Epoch 14/500
6/6 [==============================] - 2s 386ms/step - loss: 3.5053 - val_loss: 5.1233
Epoch 15/500
6/6 [==============================] - 2s 389ms/step - loss: 3.8120 - val_loss: 4.4539
Epoch 16/500
6/6 [==============================] - 2s 393ms/step - loss: 3.6717 - val_loss: 3.6430
Epoch 17/500
6/6 [==============================] - 2s 394ms/step - loss: 3.8449 - val_loss: 3.0910
Epoch 18/500
6/6 [==============================] - 2s 383ms/step - loss: 3.5770 - val_loss: 3.1092
Epoch 19/500
6/6 [==============================] - 2s 396ms/step - loss: 3.3909 - val_loss: 2.9531
Epoch 20/500
6/6 [==============================] - 2s 396ms/step - loss: 3.3265 - val_loss: 2.5751
Epoch 21/500
6/6 [==============================] - 2s 393ms/step - loss: 3.2578 - val_loss: 2.6691
Epoch 22/500
6/6 [==============================] - 2s 399ms/step - loss: 3.4847 - val_loss: 2.8450
Epoch 23/500
6/6 [==============================] - 2s 374ms/step - loss: 3.3535 - val_loss: 2.7898
Epoch 24/500
6/6 [==============================] - 2s 402ms/step - loss: 3.2835 - val_loss: 2.8280
Epoch 25/500
6/6 [==============================] - 2s 393ms/step - loss: 3.0408 - val_loss: 2.7652
Epoch 26/500
6/6 [==============================] - 2s 395ms/step - loss: 3.0879 - val_loss: 2.7263
Epoch 27/500
6/6 [==============================] - 2s 389ms/step - loss: 3.1576 - val_loss: 2.5861
Epoch 28/500
6/6 [==============================] - 2s 402ms/step - loss: 3.0757 - val_loss: 2.5537
Epoch 29/500
6/6 [==============================] - 2s 393ms/step - loss: 2.9941 - val_loss: 2.4627
Epoch 30/500
6/6 [==============================] - 2s 399ms/step - loss: 2.9609 - val_loss: 2.3986
Epoch 31/500
6/6 [==============================] - 2s 374ms/step - loss: 3.0258 - val_loss: 2.3505
Epoch 32/500
6/6 [==============================] - 2s 399ms/step - loss: 3.0698 - val_loss: 2.2622
Epoch 33/500
6/6 [==============================] - 2s 383ms/step - loss: 3.2569 - val_loss: 2.2663
Epoch 34/500
6/6 [==============================] - 2s 389ms/step - loss: 2.9455 - val_loss: 2.2455
Epoch 35/500
6/6 [==============================] - 2s 396ms/step - loss: 2.9484 - val_loss: 2.2011
Epoch 36/500
6/6 [==============================] - 2s 389ms/step - loss: 3.1214 - val_loss: 2.1854
Epoch 37/500
6/6 [==============================] - 2s 392ms/step - loss: 3.1752 - val_loss: 2.2178
Epoch 38/500
6/6 [==============================] - 2s 396ms/step - loss: 2.7629 - val_loss: 2.2652
Epoch 39/500
6/6 [==============================] - 2s 389ms/step - loss: 3.0608 - val_loss: 2.2863
Epoch 40/500
6/6 [==============================] - 2s 393ms/step - loss: 2.8679 - val_loss: 2.2746
Epoch 41/500
6/6 [==============================] - 2s 402ms/step - loss: 2.9903 - val_loss: 2.2970
Epoch 42/500
6/6 [==============================] - 2s 389ms/step - loss: 2.8023 - val_loss: 2.3931
Epoch 43/500
6/6 [==============================] - 2s 383ms/step - loss: 2.8017 - val_loss: 2.3708
Epoch 44/500
6/6 [==============================] - 2s 392ms/step - loss: 2.9386 - val_loss: 2.3130
Epoch 45/500
6/6 [==============================] - 2s 393ms/step - loss: 2.9908 - val_loss: 2.2946
Epoch 46/500
6/6 [==============================] - 2s 386ms/step - loss: 2.8068 - val_loss: 2.3512
Epoch 47/500
6/6 [==============================] - 2s 402ms/step - loss: 2.7936 - val_loss: 2.3138
Epoch 48/500
6/6 [==============================] - 2s 383ms/step - loss: 2.8404 - val_loss: 2.2119
Epoch 49/500
6/6 [==============================] - 2s 411ms/step - loss: 2.8881 - val_loss: 2.1671
Epoch 50/500
6/6 [==============================] - 2s 393ms/step - loss: 2.7233 - val_loss: 2.2104
Epoch 51/500
6/6 [==============================] - 3s 457ms/step - loss: 2.8933 - val_loss: 2.3103
Epoch 52/500
6/6 [==============================] - 3s 407ms/step - loss: 2.8625 - val_loss: 2.2457
Epoch 53/500
6/6 [==============================] - 2s 392ms/step - loss: 2.8198 - val_loss: 2.3574
Epoch 54/500
6/6 [==============================] - 3s 436ms/step - loss: 2.8230 - val_loss: 2.2263
Epoch 55/500
6/6 [==============================] - 3s 418ms/step - loss: 2.8129 - val_loss: 2.1796
Epoch 56/500
6/6 [==============================] - 2s 386ms/step - loss: 2.6834 - val_loss: 2.2182
Epoch 57/500
6/6 [==============================] - 2s 389ms/step - loss: 2.8226 - val_loss: 2.2556
Epoch 58/500
6/6 [==============================] - 3s 430ms/step - loss: 2.7307 - val_loss: 2.1718
Epoch 59/500
6/6 [==============================] - 3s 416ms/step - loss: 2.6539 - val_loss: 2.1206
Epoch 60/500
6/6 [==============================] - 2s 398ms/step - loss: 2.7312 - val_loss: 2.1636
Epoch 61/500
6/6 [==============================] - 2s 393ms/step - loss: 2.7438 - val_loss: 2.1509
Epoch 62/500
6/6 [==============================] - 3s 439ms/step - loss: 2.7543 - val_loss: 2.1097
Epoch 63/500
6/6 [==============================] - 2s 386ms/step - loss: 2.7923 - val_loss: 2.1410
Epoch 64/500
6/6 [==============================] - 2s 392ms/step - loss: 2.7065 - val_loss: 2.1752
Epoch 65/500
6/6 [==============================] - 2s 389ms/step - loss: 2.7542 - val_loss: 2.2184
Epoch 66/500
6/6 [==============================] - 2s 389ms/step - loss: 2.6155 - val_loss: 2.1348
Epoch 67/500
6/6 [==============================] - 3s 424ms/step - loss: 2.7577 - val_loss: 2.1034
Epoch 68/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6451 - val_loss: 2.1799
Epoch 69/500
6/6 [==============================] - 2s 399ms/step - loss: 2.8201 - val_loss: 2.1265
Epoch 70/500
6/6 [==============================] - 3s 436ms/step - loss: 2.7297 - val_loss: 2.0469
Epoch 71/500
6/6 [==============================] - 2s 380ms/step - loss: 2.8175 - val_loss: 2.0622
Epoch 72/500
6/6 [==============================] - 2s 396ms/step - loss: 2.8017 - val_loss: 2.1239
Epoch 73/500
6/6 [==============================] - 2s 386ms/step - loss: 2.8685 - val_loss: 2.0531
Epoch 74/500
6/6 [==============================] - 3s 433ms/step - loss: 2.7542 - val_loss: 2.0085
Epoch 75/500
6/6 [==============================] - 3s 430ms/step - loss: 2.4923 - val_loss: 2.0052
Epoch 76/500
6/6 [==============================] - 2s 389ms/step - loss: 2.6058 - val_loss: 2.0581
Epoch 77/500
6/6 [==============================] - 2s 402ms/step - loss: 2.6325 - val_loss: 2.0618
Epoch 78/500
6/6 [==============================] - 2s 395ms/step - loss: 2.6709 - val_loss: 2.0813
Epoch 79/500
6/6 [==============================] - 2s 395ms/step - loss: 2.6048 - val_loss: 2.1044
Epoch 80/500
6/6 [==============================] - 2s 399ms/step - loss: 2.6755 - val_loss: 2.1048
Epoch 81/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4134 - val_loss: 2.1203
Epoch 82/500
6/6 [==============================] - 2s 392ms/step - loss: 2.6001 - val_loss: 2.2129
Epoch 83/500
6/6 [==============================] - 2s 392ms/step - loss: 2.6017 - val_loss: 2.1685
Epoch 84/500
6/6 [==============================] - 2s 389ms/step - loss: 2.6848 - val_loss: 2.1143
Epoch 85/500
6/6 [==============================] - 2s 386ms/step - loss: 2.6396 - val_loss: 2.1307
Epoch 86/500
6/6 [==============================] - 2s 389ms/step - loss: 2.7596 - val_loss: 2.0718
Epoch 87/500
6/6 [==============================] - 2s 386ms/step - loss: 2.5223 - val_loss: 2.0552
Epoch 88/500
6/6 [==============================] - 2s 395ms/step - loss: 2.4169 - val_loss: 2.0908
Epoch 89/500
6/6 [==============================] - 2s 386ms/step - loss: 2.5197 - val_loss: 2.0561
Epoch 90/500
6/6 [==============================] - 2s 389ms/step - loss: 2.6058 - val_loss: 2.0675
Epoch 91/500
6/6 [==============================] - 2s 396ms/step - loss: 2.5198 - val_loss: 2.0712
Epoch 92/500
6/6 [==============================] - 2s 386ms/step - loss: 2.4955 - val_loss: 2.0280
Epoch 93/500
6/6 [==============================] - 3s 424ms/step - loss: 2.4489 - val_loss: 1.9924
Epoch 94/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4627 - val_loss: 2.0257
Epoch 95/500
6/6 [==============================] - 2s 405ms/step - loss: 2.5475 - val_loss: 2.0097
Epoch 96/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4975 - val_loss: 2.0022
Epoch 97/500
6/6 [==============================] - 2s 411ms/step - loss: 2.7493 - val_loss: 2.0068
Epoch 98/500
6/6 [==============================] - 2s 405ms/step - loss: 2.4647 - val_loss: 2.0559
Epoch 99/500
6/6 [==============================] - 2s 400ms/step - loss: 2.5474 - val_loss: 2.0578
Epoch 100/500
6/6 [==============================] - 2s 402ms/step - loss: 2.5412 - val_loss: 2.0413
Epoch 101/500
6/6 [==============================] - 2s 398ms/step - loss: 2.4721 - val_loss: 2.0669
Epoch 102/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6496 - val_loss: 2.0616
Epoch 103/500
6/6 [==============================] - 2s 399ms/step - loss: 2.7026 - val_loss: 2.0048
Epoch 104/500
6/6 [==============================] - 3s 474ms/step - loss: 2.4164 - val_loss: 1.9813
Epoch 105/500
6/6 [==============================] - 2s 365ms/step - loss: 2.4874 - val_loss: 2.0156
Epoch 106/500
6/6 [==============================] - 2s 387ms/step - loss: 2.7038 - val_loss: 2.0420
Epoch 107/500
6/6 [==============================] - 2s 409ms/step - loss: 2.3836 - val_loss: 2.0296
Epoch 108/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5347 - val_loss: 2.0244
Epoch 109/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4083 - val_loss: 2.0439
Epoch 110/500
6/6 [==============================] - 2s 380ms/step - loss: 2.3557 - val_loss: 2.1119
Epoch 111/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5658 - val_loss: 2.1322
Epoch 112/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5439 - val_loss: 2.1033
Epoch 113/500
6/6 [==============================] - 2s 390ms/step - loss: 2.2573 - val_loss: 2.1105
Epoch 114/500
6/6 [==============================] - 2s 387ms/step - loss: 2.4309 - val_loss: 2.1646
Epoch 115/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6004 - val_loss: 2.1457
Epoch 116/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5879 - val_loss: 2.1794
Epoch 117/500
6/6 [==============================] - 2s 394ms/step - loss: 2.3380 - val_loss: 2.1562
Epoch 118/500
6/6 [==============================] - 2s 384ms/step - loss: 2.4301 - val_loss: 2.0857
Epoch 119/500
6/6 [==============================] - 2s 384ms/step - loss: 2.5505 - val_loss: 2.0984
Epoch 120/500
6/6 [==============================] - 2s 390ms/step - loss: 2.3934 - val_loss: 2.1004
Epoch 121/500
6/6 [==============================] - 2s 409ms/step - loss: 2.6342 - val_loss: 2.0969
Epoch 122/500
6/6 [==============================] - 2s 387ms/step - loss: 2.3066 - val_loss: 2.0548
Epoch 123/500
6/6 [==============================] - 2s 378ms/step - loss: 2.4671 - val_loss: 2.0530
Epoch 124/500
6/6 [==============================] - 3s 560ms/step - loss: 2.3600 - val_loss: 2.0429
Epoch 124: early stopping
Epoch 1/500
6/6 [==============================] - 36s 812ms/step - loss: 3.4572 - val_loss: 2.0808
Epoch 2/500
6/6 [==============================] - 3s 437ms/step - loss: 2.9934 - val_loss: 2.3744
Epoch 3/500
6/6 [==============================] - 3s 453ms/step - loss: 2.7247 - val_loss: 2.7068
Epoch 4/500
6/6 [==============================] - 3s 464ms/step - loss: 2.6627 - val_loss: 2.8157
Epoch 5/500
6/6 [==============================] - 3s 473ms/step - loss: 2.5249 - val_loss: 2.5195
Epoch 6/500
6/6 [==============================] - 3s 512ms/step - loss: 2.3255 - val_loss: 2.3995
Epoch 7/500
6/6 [==============================] - 3s 487ms/step - loss: 2.1856 - val_loss: 2.3795
Epoch 8/500
6/6 [==============================] - 3s 453ms/step - loss: 2.0384 - val_loss: 2.4667
Epoch 9/500
6/6 [==============================] - 3s 465ms/step - loss: 2.0518 - val_loss: 2.6667
Epoch 10/500
6/6 [==============================] - 3s 468ms/step - loss: 1.9651 - val_loss: 2.7353
Epoch 11/500
6/6 [==============================] - 3s 487ms/step - loss: 2.1236 - val_loss: 2.4553
Epoch 12/500
6/6 [==============================] - 3s 525ms/step - loss: 1.9386 - val_loss: 2.3197
Epoch 13/500
6/6 [==============================] - 3s 545ms/step - loss: 1.9444 - val_loss: 2.1657
Epoch 14/500
6/6 [==============================] - 3s 468ms/step - loss: 1.9096 - val_loss: 2.1869
Epoch 15/500
6/6 [==============================] - 3s 478ms/step - loss: 1.7844 - val_loss: 2.1908
Epoch 16/500
6/6 [==============================] - 3s 525ms/step - loss: 1.7210 - val_loss: 2.1654
Epoch 17/500
6/6 [==============================] - 3s 522ms/step - loss: 1.8401 - val_loss: 2.0144
Epoch 18/500
6/6 [==============================] - 3s 513ms/step - loss: 1.7804 - val_loss: 1.8208
Epoch 19/500
6/6 [==============================] - 3s 531ms/step - loss: 1.7373 - val_loss: 1.8202
Epoch 20/500
6/6 [==============================] - 3s 519ms/step - loss: 1.6968 - val_loss: 1.8022
Epoch 21/500
6/6 [==============================] - 3s 516ms/step - loss: 1.6252 - val_loss: 1.7848
Epoch 22/500
6/6 [==============================] - 3s 535ms/step - loss: 1.6955 - val_loss: 1.7610
Epoch 23/500
6/6 [==============================] - 3s 499ms/step - loss: 1.6199 - val_loss: 1.7641
Epoch 24/500
6/6 [==============================] - 3s 489ms/step - loss: 1.6861 - val_loss: 1.7896
Epoch 25/500
6/6 [==============================] - 3s 501ms/step - loss: 1.8105 - val_loss: 1.8787
Epoch 26/500
6/6 [==============================] - 3s 523ms/step - loss: 1.7826 - val_loss: 1.7433
Epoch 27/500
6/6 [==============================] - 4s 522ms/step - loss: 1.6686 - val_loss: 1.7235
Epoch 28/500
6/6 [==============================] - 3s 483ms/step - loss: 1.5956 - val_loss: 1.7708
Epoch 29/500
6/6 [==============================] - 3s 470ms/step - loss: 1.5883 - val_loss: 1.7840
Epoch 30/500
6/6 [==============================] - 3s 498ms/step - loss: 1.5619 - val_loss: 1.7388
Epoch 31/500
6/6 [==============================] - 3s 533ms/step - loss: 1.5231 - val_loss: 1.6626
Epoch 32/500
6/6 [==============================] - 3s 524ms/step - loss: 1.5586 - val_loss: 1.5999
Epoch 33/500
6/6 [==============================] - 3s 490ms/step - loss: 1.5908 - val_loss: 1.6091
Epoch 34/500
6/6 [==============================] - 3s 492ms/step - loss: 1.6201 - val_loss: 1.6065
Epoch 35/500
6/6 [==============================] - 3s 504ms/step - loss: 1.6151 - val_loss: 1.6262
Epoch 36/500
6/6 [==============================] - 3s 499ms/step - loss: 1.4580 - val_loss: 1.6068
Epoch 37/500
6/6 [==============================] - 3s 488ms/step - loss: 1.5510 - val_loss: 1.6098
Epoch 38/500
6/6 [==============================] - 3s 496ms/step - loss: 1.5048 - val_loss: 1.6625
Epoch 39/500
6/6 [==============================] - 3s 504ms/step - loss: 1.5433 - val_loss: 1.6642
Epoch 40/500
6/6 [==============================] - 3s 504ms/step - loss: 1.4648 - val_loss: 1.6330
Epoch 41/500
6/6 [==============================] - 3s 487ms/step - loss: 1.4985 - val_loss: 1.6021
Epoch 42/500
6/6 [==============================] - 3s 511ms/step - loss: 1.5120 - val_loss: 1.6280
Epoch 43/500
6/6 [==============================] - 3s 531ms/step - loss: 1.4840 - val_loss: 1.5995
Epoch 44/500
6/6 [==============================] - 3s 475ms/step - loss: 1.4810 - val_loss: 1.6106
Epoch 45/500
6/6 [==============================] - 3s 497ms/step - loss: 1.5754 - val_loss: 1.6131
Epoch 46/500
6/6 [==============================] - 3s 491ms/step - loss: 1.3510 - val_loss: 1.6212
Epoch 47/500
6/6 [==============================] - 3s 484ms/step - loss: 1.5039 - val_loss: 1.6133
Epoch 48/500
6/6 [==============================] - 3s 502ms/step - loss: 1.5665 - val_loss: 1.6284
Epoch 49/500
6/6 [==============================] - 3s 485ms/step - loss: 1.4938 - val_loss: 1.6495
Epoch 50/500
6/6 [==============================] - 3s 495ms/step - loss: 1.5067 - val_loss: 1.6680
Epoch 51/500
6/6 [==============================] - 3s 501ms/step - loss: 1.5195 - val_loss: 1.6425
Epoch 52/500
6/6 [==============================] - 3s 491ms/step - loss: 1.5520 - val_loss: 1.6352
Epoch 53/500
6/6 [==============================] - 3s 505ms/step - loss: 1.3583 - val_loss: 1.6727
Epoch 54/500
6/6 [==============================] - 3s 502ms/step - loss: 1.4997 - val_loss: 1.6752
Epoch 55/500
6/6 [==============================] - 3s 499ms/step - loss: 1.4523 - val_loss: 1.6487
Epoch 56/500
6/6 [==============================] - 3s 491ms/step - loss: 1.3919 - val_loss: 1.6002
Epoch 57/500
6/6 [==============================] - 3s 547ms/step - loss: 1.4758 - val_loss: 1.5783
Epoch 58/500
6/6 [==============================] - 4s 556ms/step - loss: 1.4362 - val_loss: 1.5629
Epoch 59/500
6/6 [==============================] - 3s 536ms/step - loss: 1.5082 - val_loss: 1.5563
Epoch 60/500
6/6 [==============================] - 3s 485ms/step - loss: 1.5536 - val_loss: 1.5825
Epoch 61/500
6/6 [==============================] - 3s 499ms/step - loss: 1.3243 - val_loss: 1.6084
Epoch 62/500
6/6 [==============================] - 3s 507ms/step - loss: 1.4412 - val_loss: 1.5933
Epoch 63/500
6/6 [==============================] - 3s 502ms/step - loss: 1.3224 - val_loss: 1.5585
Epoch 64/500
6/6 [==============================] - 3s 548ms/step - loss: 1.3197 - val_loss: 1.5320
Epoch 65/500
6/6 [==============================] - 3s 527ms/step - loss: 1.4154 - val_loss: 1.5182
Epoch 66/500
6/6 [==============================] - 3s 493ms/step - loss: 1.4069 - val_loss: 1.5231
Epoch 67/500
6/6 [==============================] - 3s 535ms/step - loss: 1.3598 - val_loss: 1.5116
Epoch 68/500
6/6 [==============================] - 3s 529ms/step - loss: 1.3344 - val_loss: 1.4952
Epoch 69/500
6/6 [==============================] - 4s 542ms/step - loss: 1.4886 - val_loss: 1.4703
Epoch 70/500
6/6 [==============================] - 3s 486ms/step - loss: 1.4186 - val_loss: 1.5086
Epoch 71/500
6/6 [==============================] - 3s 477ms/step - loss: 1.3327 - val_loss: 1.5004
Epoch 72/500
6/6 [==============================] - 3s 480ms/step - loss: 1.4337 - val_loss: 1.5024
Epoch 73/500
6/6 [==============================] - 3s 513ms/step - loss: 1.3322 - val_loss: 1.4807
Epoch 74/500
6/6 [==============================] - 3s 508ms/step - loss: 1.3633 - val_loss: 1.4840
Epoch 75/500
6/6 [==============================] - 3s 545ms/step - loss: 1.3516 - val_loss: 1.4681
Epoch 76/500
6/6 [==============================] - 3s 525ms/step - loss: 1.4397 - val_loss: 1.4996
Epoch 77/500
6/6 [==============================] - 3s 497ms/step - loss: 1.3382 - val_loss: 1.4940
Epoch 78/500
6/6 [==============================] - 3s 528ms/step - loss: 1.3245 - val_loss: 1.5220
Epoch 79/500
6/6 [==============================] - 3s 538ms/step - loss: 1.3917 - val_loss: 1.5229
Epoch 80/500
6/6 [==============================] - 4s 556ms/step - loss: 1.3205 - val_loss: 1.5241
Epoch 81/500
6/6 [==============================] - 3s 519ms/step - loss: 1.4019 - val_loss: 1.5092
Epoch 82/500
6/6 [==============================] - 3s 511ms/step - loss: 1.2934 - val_loss: 1.5416
Epoch 83/500
6/6 [==============================] - 3s 519ms/step - loss: 1.2328 - val_loss: 1.5620
Epoch 84/500
6/6 [==============================] - 3s 531ms/step - loss: 1.3357 - val_loss: 1.5859
Epoch 85/500
6/6 [==============================] - 3s 516ms/step - loss: 1.3809 - val_loss: 1.5440
Epoch 86/500
6/6 [==============================] - 3s 538ms/step - loss: 1.2947 - val_loss: 1.5191
Epoch 87/500
6/6 [==============================] - 3s 532ms/step - loss: 1.4000 - val_loss: 1.5112
Epoch 88/500
6/6 [==============================] - 4s 535ms/step - loss: 1.3869 - val_loss: 1.5340
Epoch 89/500
6/6 [==============================] - 3s 515ms/step - loss: 1.3896 - val_loss: 1.5777
Epoch 90/500
6/6 [==============================] - 3s 534ms/step - loss: 1.3041 - val_loss: 1.5553
Epoch 91/500
6/6 [==============================] - 3s 529ms/step - loss: 1.2893 - val_loss: 1.5244
Epoch 92/500
6/6 [==============================] - 3s 522ms/step - loss: 1.2565 - val_loss: 1.5092
Epoch 93/500
6/6 [==============================] - 3s 521ms/step - loss: 1.2939 - val_loss: 1.5390
Epoch 94/500
6/6 [==============================] - 3s 550ms/step - loss: 1.3455 - val_loss: 1.4770
Epoch 95/500
6/6 [==============================] - 5s 824ms/step - loss: 1.2437 - val_loss: 1.4750
Epoch 95: early stopping
Epoch 1/500
6/6 [==============================] - 11s 727ms/step - loss: 9.7631 - val_loss: 8.2825
Epoch 2/500
6/6 [==============================] - 2s 406ms/step - loss: 7.8120 - val_loss: 7.1530
Epoch 3/500
6/6 [==============================] - 2s 399ms/step - loss: 6.9380 - val_loss: 5.9415
Epoch 4/500
6/6 [==============================] - 2s 396ms/step - loss: 6.6143 - val_loss: 6.2411
Epoch 5/500
6/6 [==============================] - 2s 384ms/step - loss: 5.5686 - val_loss: 6.6589
Epoch 6/500
6/6 [==============================] - 2s 412ms/step - loss: 5.2302 - val_loss: 5.5890
Epoch 7/500
6/6 [==============================] - 2s 384ms/step - loss: 5.0572 - val_loss: 4.5930
Epoch 8/500
6/6 [==============================] - 2s 403ms/step - loss: 4.8436 - val_loss: 3.9415
Epoch 9/500
6/6 [==============================] - 2s 400ms/step - loss: 4.3629 - val_loss: 4.0042
Epoch 10/500
6/6 [==============================] - 2s 387ms/step - loss: 4.3853 - val_loss: 3.7472
Epoch 11/500
6/6 [==============================] - 2s 403ms/step - loss: 4.1230 - val_loss: 4.2118
Epoch 12/500
6/6 [==============================] - 3s 412ms/step - loss: 4.0549 - val_loss: 4.1936
Epoch 13/500
6/6 [==============================] - 3s 409ms/step - loss: 3.8565 - val_loss: 4.2510
Epoch 14/500
6/6 [==============================] - 2s 407ms/step - loss: 3.7823 - val_loss: 3.6680
Epoch 15/500
6/6 [==============================] - 2s 393ms/step - loss: 3.4828 - val_loss: 3.6991
Epoch 16/500
6/6 [==============================] - 3s 411ms/step - loss: 3.7272 - val_loss: 3.4567
Epoch 17/500
6/6 [==============================] - 2s 410ms/step - loss: 3.7294 - val_loss: 3.6367
Epoch 18/500
6/6 [==============================] - 2s 403ms/step - loss: 3.7007 - val_loss: 3.2473
Epoch 19/500
6/6 [==============================] - 2s 406ms/step - loss: 3.5894 - val_loss: 2.9793
Epoch 20/500
6/6 [==============================] - 2s 406ms/step - loss: 3.4226 - val_loss: 2.9857
Epoch 21/500
6/6 [==============================] - 2s 406ms/step - loss: 3.4666 - val_loss: 2.8243
Epoch 22/500
6/6 [==============================] - 3s 406ms/step - loss: 3.2644 - val_loss: 2.8180
Epoch 23/500
6/6 [==============================] - 2s 393ms/step - loss: 3.5001 - val_loss: 2.5424
Epoch 24/500
6/6 [==============================] - 2s 399ms/step - loss: 3.1987 - val_loss: 2.5715
Epoch 25/500
6/6 [==============================] - 2s 396ms/step - loss: 3.3635 - val_loss: 2.5072
Epoch 26/500
6/6 [==============================] - 2s 384ms/step - loss: 3.2549 - val_loss: 2.4436
Epoch 27/500
6/6 [==============================] - 2s 400ms/step - loss: 3.2604 - val_loss: 2.4339
Epoch 28/500
6/6 [==============================] - 2s 406ms/step - loss: 2.9663 - val_loss: 2.4621
Epoch 29/500
6/6 [==============================] - 2s 400ms/step - loss: 3.0659 - val_loss: 2.4756
Epoch 30/500
6/6 [==============================] - 2s 406ms/step - loss: 3.3052 - val_loss: 2.4991
Epoch 31/500
6/6 [==============================] - 3s 418ms/step - loss: 3.1707 - val_loss: 2.5368
Epoch 32/500
6/6 [==============================] - 2s 390ms/step - loss: 3.0294 - val_loss: 2.4915
Epoch 33/500
6/6 [==============================] - 2s 400ms/step - loss: 3.2257 - val_loss: 2.5385
Epoch 34/500
6/6 [==============================] - 2s 403ms/step - loss: 3.0208 - val_loss: 2.5601
Epoch 35/500
6/6 [==============================] - 2s 403ms/step - loss: 3.1579 - val_loss: 2.4239
Epoch 36/500
6/6 [==============================] - 2s 400ms/step - loss: 3.0568 - val_loss: 2.4432
Epoch 37/500
6/6 [==============================] - 3s 431ms/step - loss: 3.0778 - val_loss: 2.3870
Epoch 38/500
6/6 [==============================] - 2s 400ms/step - loss: 2.9634 - val_loss: 2.4401
Epoch 39/500
6/6 [==============================] - 2s 400ms/step - loss: 2.9786 - val_loss: 2.4772
Epoch 40/500
6/6 [==============================] - 2s 397ms/step - loss: 3.0995 - val_loss: 2.3780
Epoch 41/500
6/6 [==============================] - 2s 393ms/step - loss: 2.9290 - val_loss: 2.5254
Epoch 42/500
6/6 [==============================] - 2s 393ms/step - loss: 2.7806 - val_loss: 2.4539
Epoch 43/500
6/6 [==============================] - 2s 406ms/step - loss: 2.8006 - val_loss: 2.4506
Epoch 44/500
6/6 [==============================] - 2s 396ms/step - loss: 2.7776 - val_loss: 2.5101
Epoch 45/500
6/6 [==============================] - 2s 397ms/step - loss: 3.0115 - val_loss: 2.2946
Epoch 46/500
6/6 [==============================] - 2s 409ms/step - loss: 3.0859 - val_loss: 2.2577
Epoch 47/500
6/6 [==============================] - 2s 393ms/step - loss: 2.9284 - val_loss: 2.2229
Epoch 48/500
6/6 [==============================] - 2s 393ms/step - loss: 2.8716 - val_loss: 2.2636
Epoch 49/500
6/6 [==============================] - 2s 399ms/step - loss: 2.8155 - val_loss: 2.2226
Epoch 50/500
6/6 [==============================] - 2s 403ms/step - loss: 3.0631 - val_loss: 2.3146
Epoch 51/500
6/6 [==============================] - 3s 465ms/step - loss: 2.8524 - val_loss: 2.2806
Epoch 52/500
6/6 [==============================] - 3s 464ms/step - loss: 2.9444 - val_loss: 2.2449
Epoch 53/500
6/6 [==============================] - 3s 431ms/step - loss: 2.8610 - val_loss: 2.2355
Epoch 54/500
6/6 [==============================] - 3s 421ms/step - loss: 2.7447 - val_loss: 2.1737
Epoch 55/500
6/6 [==============================] - 2s 362ms/step - loss: 2.6710 - val_loss: 2.2735
Epoch 56/500
6/6 [==============================] - 2s 403ms/step - loss: 2.8480 - val_loss: 2.1988
Epoch 57/500
6/6 [==============================] - 2s 390ms/step - loss: 2.7408 - val_loss: 2.2230
Epoch 58/500
6/6 [==============================] - 2s 401ms/step - loss: 2.7873 - val_loss: 2.2471
Epoch 59/500
6/6 [==============================] - 2s 396ms/step - loss: 2.8211 - val_loss: 2.1920
Epoch 60/500
6/6 [==============================] - 2s 375ms/step - loss: 2.5799 - val_loss: 2.2103
Epoch 61/500
6/6 [==============================] - 2s 393ms/step - loss: 2.8172 - val_loss: 2.1865
Epoch 62/500
6/6 [==============================] - 3s 431ms/step - loss: 3.0102 - val_loss: 2.1466
Epoch 63/500
6/6 [==============================] - 3s 449ms/step - loss: 2.6409 - val_loss: 2.1258
Epoch 64/500
6/6 [==============================] - 3s 387ms/step - loss: 2.6482 - val_loss: 2.1532
Epoch 65/500
6/6 [==============================] - 2s 399ms/step - loss: 2.8218 - val_loss: 2.1374
Epoch 66/500
6/6 [==============================] - 2s 387ms/step - loss: 2.6162 - val_loss: 2.1641
Epoch 67/500
6/6 [==============================] - 2s 400ms/step - loss: 2.7017 - val_loss: 2.2083
Epoch 68/500
6/6 [==============================] - 3s 432ms/step - loss: 2.7583 - val_loss: 2.0969
Epoch 69/500
6/6 [==============================] - 3s 431ms/step - loss: 2.7497 - val_loss: 2.0723
Epoch 70/500
6/6 [==============================] - 2s 399ms/step - loss: 2.6658 - val_loss: 2.1124
Epoch 71/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5718 - val_loss: 2.1360
Epoch 72/500
6/6 [==============================] - 2s 390ms/step - loss: 2.7984 - val_loss: 2.1208
Epoch 73/500
6/6 [==============================] - 2s 387ms/step - loss: 2.6726 - val_loss: 2.1288
Epoch 74/500
6/6 [==============================] - 2s 393ms/step - loss: 2.6829 - val_loss: 2.1761
Epoch 75/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5524 - val_loss: 2.1639
Epoch 76/500
6/6 [==============================] - 2s 400ms/step - loss: 2.6430 - val_loss: 2.1501
Epoch 77/500
6/6 [==============================] - 2s 399ms/step - loss: 2.6985 - val_loss: 2.1800
Epoch 78/500
6/6 [==============================] - 2s 387ms/step - loss: 2.4859 - val_loss: 2.1645
Epoch 79/500
6/6 [==============================] - 2s 390ms/step - loss: 2.7368 - val_loss: 2.1490
Epoch 80/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6752 - val_loss: 2.1977
Epoch 81/500
6/6 [==============================] - 2s 409ms/step - loss: 2.6630 - val_loss: 2.2162
Epoch 82/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5619 - val_loss: 2.1509
Epoch 83/500
6/6 [==============================] - 2s 400ms/step - loss: 2.5133 - val_loss: 2.1953
Epoch 84/500
6/6 [==============================] - 2s 403ms/step - loss: 2.5557 - val_loss: 2.1526
Epoch 85/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4903 - val_loss: 2.1316
Epoch 86/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5477 - val_loss: 2.1514
Epoch 87/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5951 - val_loss: 2.1407
Epoch 88/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4760 - val_loss: 2.1079
Epoch 89/500
6/6 [==============================] - 3s 437ms/step - loss: 2.5423 - val_loss: 2.0705
Epoch 90/500
6/6 [==============================] - 2s 406ms/step - loss: 2.7056 - val_loss: 2.0812
Epoch 91/500
6/6 [==============================] - 2s 393ms/step - loss: 2.8398 - val_loss: 2.1540
Epoch 92/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4428 - val_loss: 2.1080
Epoch 93/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4223 - val_loss: 2.0853
Epoch 94/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5518 - val_loss: 2.1045
Epoch 95/500
6/6 [==============================] - 2s 412ms/step - loss: 2.4839 - val_loss: 2.1101
Epoch 96/500
6/6 [==============================] - 2s 397ms/step - loss: 2.5652 - val_loss: 2.0797
Epoch 97/500
6/6 [==============================] - 2s 396ms/step - loss: 2.5144 - val_loss: 2.0773
Epoch 98/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6451 - val_loss: 2.0707
Epoch 99/500
6/6 [==============================] - 3s 431ms/step - loss: 2.6113 - val_loss: 2.0434
Epoch 100/500
6/6 [==============================] - 3s 440ms/step - loss: 2.4804 - val_loss: 2.0299
Epoch 101/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4452 - val_loss: 2.0788
Epoch 102/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6083 - val_loss: 2.0940
Epoch 103/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5540 - val_loss: 2.0508
Epoch 104/500
6/6 [==============================] - 3s 415ms/step - loss: 2.4733 - val_loss: 2.0016
Epoch 105/500
6/6 [==============================] - 2s 385ms/step - loss: 2.4930 - val_loss: 2.0277
Epoch 106/500
6/6 [==============================] - 3s 401ms/step - loss: 2.5260 - val_loss: 2.0641
Epoch 107/500
6/6 [==============================] - 2s 403ms/step - loss: 2.5641 - val_loss: 2.0709
Epoch 108/500
6/6 [==============================] - 2s 389ms/step - loss: 2.4504 - val_loss: 2.0901
Epoch 109/500
6/6 [==============================] - 2s 396ms/step - loss: 2.7054 - val_loss: 2.0261
Epoch 110/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6082 - val_loss: 2.0797
Epoch 111/500
6/6 [==============================] - 2s 393ms/step - loss: 2.4748 - val_loss: 2.0477
Epoch 112/500
6/6 [==============================] - 2s 399ms/step - loss: 2.6864 - val_loss: 2.0334
Epoch 113/500
6/6 [==============================] - 2s 386ms/step - loss: 2.5674 - val_loss: 2.0681
Epoch 114/500
6/6 [==============================] - 2s 392ms/step - loss: 2.5108 - val_loss: 2.0336
Epoch 115/500
6/6 [==============================] - 2s 386ms/step - loss: 2.5526 - val_loss: 2.0112
Epoch 116/500
6/6 [==============================] - 2s 405ms/step - loss: 2.3964 - val_loss: 2.0027
Epoch 117/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5509 - val_loss: 2.0792
Epoch 118/500
6/6 [==============================] - 2s 386ms/step - loss: 2.5594 - val_loss: 2.0796
Epoch 119/500
6/6 [==============================] - 2s 386ms/step - loss: 2.4121 - val_loss: 2.0693
Epoch 120/500
6/6 [==============================] - 2s 380ms/step - loss: 2.3507 - val_loss: 2.0289
Epoch 121/500
6/6 [==============================] - 3s 427ms/step - loss: 2.4235 - val_loss: 1.9985
Epoch 122/500
6/6 [==============================] - 3s 446ms/step - loss: 2.4816 - val_loss: 1.9949
Epoch 123/500
6/6 [==============================] - 2s 358ms/step - loss: 2.5047 - val_loss: 1.9959
Epoch 124/500
6/6 [==============================] - 2s 390ms/step - loss: 2.3613 - val_loss: 2.0264
Epoch 125/500
6/6 [==============================] - 2s 396ms/step - loss: 2.4423 - val_loss: 2.0443
Epoch 126/500
6/6 [==============================] - 2s 381ms/step - loss: 2.2868 - val_loss: 2.0494
Epoch 127/500
6/6 [==============================] - 2s 396ms/step - loss: 2.5475 - val_loss: 2.0357
Epoch 128/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4020 - val_loss: 2.0563
Epoch 129/500
6/6 [==============================] - 2s 396ms/step - loss: 2.5196 - val_loss: 2.0260
Epoch 130/500
6/6 [==============================] - 2s 387ms/step - loss: 2.4502 - val_loss: 2.0396
Epoch 131/500
6/6 [==============================] - 2s 402ms/step - loss: 2.4737 - val_loss: 2.0584
Epoch 132/500
6/6 [==============================] - 2s 377ms/step - loss: 2.4392 - val_loss: 2.0430
Epoch 133/500
6/6 [==============================] - 2s 396ms/step - loss: 2.3697 - val_loss: 2.0249
Epoch 134/500
6/6 [==============================] - 3s 430ms/step - loss: 2.2685 - val_loss: 1.9919
Epoch 135/500
6/6 [==============================] - 3s 443ms/step - loss: 2.2659 - val_loss: 1.9850
Epoch 136/500
6/6 [==============================] - 2s 365ms/step - loss: 2.5760 - val_loss: 2.0193
Epoch 137/500
6/6 [==============================] - 2s 406ms/step - loss: 2.5317 - val_loss: 2.0319
Epoch 138/500
6/6 [==============================] - 2s 387ms/step - loss: 2.3922 - val_loss: 2.0523
Epoch 139/500
6/6 [==============================] - 2s 393ms/step - loss: 2.4597 - val_loss: 2.0894
Epoch 140/500
6/6 [==============================] - 2s 380ms/step - loss: 2.5459 - val_loss: 2.1134
Epoch 141/500
6/6 [==============================] - 2s 404ms/step - loss: 2.3784 - val_loss: 2.1196
Epoch 142/500
6/6 [==============================] - 2s 383ms/step - loss: 2.2296 - val_loss: 2.1270
Epoch 143/500
6/6 [==============================] - 2s 403ms/step - loss: 2.4656 - val_loss: 2.1622
Epoch 144/500
6/6 [==============================] - 2s 384ms/step - loss: 2.4758 - val_loss: 2.1231
Epoch 145/500
6/6 [==============================] - 2s 399ms/step - loss: 2.5187 - val_loss: 2.1341
Epoch 146/500
6/6 [==============================] - 2s 402ms/step - loss: 2.3437 - val_loss: 2.1195
Epoch 147/500
6/6 [==============================] - 2s 384ms/step - loss: 2.3229 - val_loss: 2.1305
Epoch 148/500
6/6 [==============================] - 2s 401ms/step - loss: 2.5095 - val_loss: 2.0628
Epoch 149/500
6/6 [==============================] - 2s 403ms/step - loss: 2.3903 - val_loss: 1.9946
Epoch 150/500
6/6 [==============================] - 3s 450ms/step - loss: 2.3917 - val_loss: 1.9523
Epoch 151/500
6/6 [==============================] - 2s 399ms/step - loss: 2.4756 - val_loss: 1.9709
Epoch 152/500
6/6 [==============================] - 2s 402ms/step - loss: 2.3575 - val_loss: 1.9635
Epoch 153/500
6/6 [==============================] - 2s 399ms/step - loss: 2.3952 - val_loss: 1.9655
Epoch 154/500
6/6 [==============================] - 3s 417ms/step - loss: 2.5318 - val_loss: 1.9629
Epoch 155/500
6/6 [==============================] - 2s 392ms/step - loss: 2.4406 - val_loss: 2.0204
Epoch 156/500
6/6 [==============================] - 2s 399ms/step - loss: 2.5177 - val_loss: 1.9839
Epoch 157/500
6/6 [==============================] - 3s 443ms/step - loss: 2.3291 - val_loss: 1.9258
Epoch 158/500
6/6 [==============================] - 2s 365ms/step - loss: 2.5263 - val_loss: 1.9461
Epoch 159/500
6/6 [==============================] - 2s 387ms/step - loss: 2.4348 - val_loss: 2.0189
Epoch 160/500
6/6 [==============================] - 2s 387ms/step - loss: 2.3168 - val_loss: 2.0565
Epoch 161/500
6/6 [==============================] - 2s 390ms/step - loss: 2.3334 - val_loss: 2.0859
Epoch 162/500
6/6 [==============================] - 2s 393ms/step - loss: 2.4969 - val_loss: 2.0418
Epoch 163/500
6/6 [==============================] - 2s 387ms/step - loss: 2.4028 - val_loss: 2.0380
Epoch 164/500
6/6 [==============================] - 2s 374ms/step - loss: 2.1832 - val_loss: 2.0396
Epoch 165/500
6/6 [==============================] - 2s 393ms/step - loss: 2.2654 - val_loss: 2.0301
Epoch 166/500
6/6 [==============================] - 2s 387ms/step - loss: 2.3533 - val_loss: 2.0101
Epoch 167/500
6/6 [==============================] - 2s 390ms/step - loss: 2.2350 - val_loss: 2.0391
Epoch 168/500
6/6 [==============================] - 2s 381ms/step - loss: 2.5290 - val_loss: 2.0288
Epoch 169/500
6/6 [==============================] - 2s 378ms/step - loss: 2.4075 - val_loss: 2.0207
Epoch 170/500
6/6 [==============================] - 2s 393ms/step - loss: 2.2825 - val_loss: 2.0255
Epoch 171/500
6/6 [==============================] - 2s 396ms/step - loss: 2.2527 - val_loss: 1.9861
Epoch 172/500
6/6 [==============================] - 2s 390ms/step - loss: 2.3178 - val_loss: 1.9774
Epoch 173/500
6/6 [==============================] - 2s 390ms/step - loss: 2.2800 - val_loss: 1.9506
Epoch 174/500
6/6 [==============================] - 3s 434ms/step - loss: 2.3683 - val_loss: 1.9118
Epoch 175/500
6/6 [==============================] - 2s 402ms/step - loss: 2.3376 - val_loss: 1.9332
Epoch 176/500
6/6 [==============================] - 2s 406ms/step - loss: 2.3169 - val_loss: 1.9573
Epoch 177/500
6/6 [==============================] - 2s 387ms/step - loss: 2.2773 - val_loss: 1.9946
Epoch 178/500
6/6 [==============================] - 2s 409ms/step - loss: 2.3582 - val_loss: 2.0119
Epoch 179/500
6/6 [==============================] - 2s 387ms/step - loss: 2.2851 - val_loss: 2.0031
Epoch 180/500
6/6 [==============================] - 2s 396ms/step - loss: 2.1308 - val_loss: 2.0337
Epoch 181/500
6/6 [==============================] - 2s 407ms/step - loss: 2.2497 - val_loss: 1.9949
Epoch 182/500
6/6 [==============================] - 2s 400ms/step - loss: 2.3486 - val_loss: 1.9649
Epoch 183/500
6/6 [==============================] - 3s 440ms/step - loss: 2.3830 - val_loss: 1.9701
Epoch 184/500
6/6 [==============================] - 2s 403ms/step - loss: 2.1875 - val_loss: 1.9636
Epoch 185/500
6/6 [==============================] - 2s 394ms/step - loss: 2.2122 - val_loss: 1.9628
Epoch 186/500
6/6 [==============================] - 2s 401ms/step - loss: 2.3231 - val_loss: 1.9727
Epoch 187/500
6/6 [==============================] - 2s 403ms/step - loss: 2.2894 - val_loss: 1.9918
Epoch 188/500
6/6 [==============================] - 2s 400ms/step - loss: 2.3808 - val_loss: 1.9974
Epoch 189/500
6/6 [==============================] - 2s 409ms/step - loss: 2.2579 - val_loss: 2.0209
Epoch 190/500
6/6 [==============================] - 2s 390ms/step - loss: 2.2475 - val_loss: 1.9875
Epoch 191/500
6/6 [==============================] - 2s 403ms/step - loss: 2.3674 - val_loss: 1.9602
Epoch 192/500
6/6 [==============================] - 2s 399ms/step - loss: 2.2573 - val_loss: 1.9690
Epoch 193/500
6/6 [==============================] - 2s 403ms/step - loss: 2.1418 - val_loss: 1.9319
Epoch 194/500
6/6 [==============================] - 3s 563ms/step - loss: 2.2019 - val_loss: 1.9230
Epoch 194: early stopping
Epoch 1/500
6/6 [==============================] - 36s 811ms/step - loss: 3.8127 - val_loss: 2.0080
Epoch 2/500
6/6 [==============================] - 3s 456ms/step - loss: 3.1270 - val_loss: 2.1386
Epoch 3/500
6/6 [==============================] - 3s 474ms/step - loss: 2.6312 - val_loss: 2.3052
Epoch 4/500
6/6 [==============================] - 3s 481ms/step - loss: 2.4677 - val_loss: 2.5053
Epoch 5/500
6/6 [==============================] - 3s 459ms/step - loss: 2.2070 - val_loss: 2.5662
Epoch 6/500
6/6 [==============================] - 3s 522ms/step - loss: 2.1475 - val_loss: 2.4458
Epoch 7/500
6/6 [==============================] - 3s 489ms/step - loss: 2.0291 - val_loss: 2.3812
Epoch 8/500
6/6 [==============================] - 3s 493ms/step - loss: 2.1209 - val_loss: 2.2587
Epoch 9/500
6/6 [==============================] - 3s 484ms/step - loss: 1.8133 - val_loss: 2.0560
Epoch 10/500
6/6 [==============================] - 3s 480ms/step - loss: 1.9674 - val_loss: 2.1160
Epoch 11/500
6/6 [==============================] - 3s 515ms/step - loss: 1.8562 - val_loss: 2.0273
Epoch 12/500
6/6 [==============================] - 3s 515ms/step - loss: 1.8204 - val_loss: 2.0222
Epoch 13/500
6/6 [==============================] - 3s 512ms/step - loss: 1.9058 - val_loss: 1.9828
Epoch 14/500
6/6 [==============================] - 3s 471ms/step - loss: 1.7929 - val_loss: 2.0208
Epoch 15/500
6/6 [==============================] - 3s 506ms/step - loss: 1.8551 - val_loss: 1.9392
Epoch 16/500
6/6 [==============================] - 3s 448ms/step - loss: 1.7704 - val_loss: 1.9683
Epoch 17/500
6/6 [==============================] - 3s 463ms/step - loss: 1.6418 - val_loss: 1.9902
Epoch 18/500
6/6 [==============================] - 3s 486ms/step - loss: 1.7029 - val_loss: 2.0344
Epoch 19/500
6/6 [==============================] - 3s 497ms/step - loss: 1.6462 - val_loss: 2.0954
Epoch 20/500
6/6 [==============================] - 3s 490ms/step - loss: 1.5961 - val_loss: 1.9948
Epoch 21/500
6/6 [==============================] - 3s 523ms/step - loss: 1.6853 - val_loss: 1.8893
Epoch 22/500
6/6 [==============================] - 3s 473ms/step - loss: 1.6645 - val_loss: 1.9305
Epoch 23/500
6/6 [==============================] - 3s 538ms/step - loss: 1.8020 - val_loss: 1.7758
Epoch 24/500
6/6 [==============================] - 3s 544ms/step - loss: 1.6862 - val_loss: 1.7578
Epoch 25/500
6/6 [==============================] - 3s 534ms/step - loss: 1.6699 - val_loss: 1.7235
Epoch 26/500
6/6 [==============================] - 3s 527ms/step - loss: 1.6270 - val_loss: 1.6262
Epoch 27/500
6/6 [==============================] - 3s 524ms/step - loss: 1.4826 - val_loss: 1.6208
Epoch 28/500
6/6 [==============================] - 3s 488ms/step - loss: 1.6333 - val_loss: 1.6544
Epoch 29/500
6/6 [==============================] - 3s 515ms/step - loss: 1.5659 - val_loss: 1.6104
Epoch 30/500
6/6 [==============================] - 3s 543ms/step - loss: 1.4229 - val_loss: 1.5616
Epoch 31/500
6/6 [==============================] - 3s 526ms/step - loss: 1.6084 - val_loss: 1.5539
Epoch 32/500
6/6 [==============================] - 3s 520ms/step - loss: 1.6742 - val_loss: 1.5446
Epoch 33/500
6/6 [==============================] - 3s 509ms/step - loss: 1.4703 - val_loss: 1.5454
Epoch 34/500
6/6 [==============================] - 3s 493ms/step - loss: 1.5345 - val_loss: 1.5646
Epoch 35/500
6/6 [==============================] - 3s 503ms/step - loss: 1.6554 - val_loss: 1.5613
Epoch 36/500
6/6 [==============================] - 3s 494ms/step - loss: 1.4855 - val_loss: 1.6110
Epoch 37/500
6/6 [==============================] - 3s 497ms/step - loss: 1.4623 - val_loss: 1.5991
Epoch 38/500
6/6 [==============================] - 3s 478ms/step - loss: 1.5992 - val_loss: 1.5723
Epoch 39/500
6/6 [==============================] - 3s 533ms/step - loss: 1.4991 - val_loss: 1.5324
Epoch 40/500
6/6 [==============================] - 4s 542ms/step - loss: 1.3940 - val_loss: 1.5308
Epoch 41/500
6/6 [==============================] - 3s 521ms/step - loss: 1.5378 - val_loss: 1.4814
Epoch 42/500
6/6 [==============================] - 3s 489ms/step - loss: 1.5592 - val_loss: 1.5231
Epoch 43/500
6/6 [==============================] - 3s 504ms/step - loss: 1.4215 - val_loss: 1.5672
Epoch 44/500
6/6 [==============================] - 3s 502ms/step - loss: 1.4988 - val_loss: 1.5808
Epoch 45/500
6/6 [==============================] - 3s 505ms/step - loss: 1.4199 - val_loss: 1.5503
Epoch 46/500
6/6 [==============================] - 3s 493ms/step - loss: 1.3634 - val_loss: 1.5141
Epoch 47/500
6/6 [==============================] - 3s 481ms/step - loss: 1.5018 - val_loss: 1.5234
Epoch 48/500
6/6 [==============================] - 3s 509ms/step - loss: 1.5222 - val_loss: 1.5388
Epoch 49/500
6/6 [==============================] - 3s 494ms/step - loss: 1.5635 - val_loss: 1.6021
Epoch 50/500
6/6 [==============================] - 3s 523ms/step - loss: 1.5320 - val_loss: 1.5764
Epoch 51/500
6/6 [==============================] - 3s 553ms/step - loss: 1.4858 - val_loss: 1.4781
Epoch 52/500
6/6 [==============================] - 3s 490ms/step - loss: 1.3169 - val_loss: 1.5039
Epoch 53/500
6/6 [==============================] - 3s 497ms/step - loss: 1.4338 - val_loss: 1.4922
Epoch 54/500
6/6 [==============================] - 3s 530ms/step - loss: 1.3184 - val_loss: 1.4763
Epoch 55/500
6/6 [==============================] - 3s 529ms/step - loss: 1.3528 - val_loss: 1.4693
Epoch 56/500
6/6 [==============================] - 3s 514ms/step - loss: 1.4186 - val_loss: 1.4548
Epoch 57/500
6/6 [==============================] - 4s 550ms/step - loss: 1.4335 - val_loss: 1.4199
Epoch 58/500
6/6 [==============================] - 3s 505ms/step - loss: 1.3287 - val_loss: 1.4276
Epoch 59/500
6/6 [==============================] - 3s 502ms/step - loss: 1.3793 - val_loss: 1.4812
Epoch 60/500
6/6 [==============================] - 3s 510ms/step - loss: 1.3817 - val_loss: 1.4888
Epoch 61/500
6/6 [==============================] - 3s 503ms/step - loss: 1.5074 - val_loss: 1.4885
Epoch 62/500
6/6 [==============================] - 3s 509ms/step - loss: 1.3750 - val_loss: 1.4700
Epoch 63/500
6/6 [==============================] - 3s 509ms/step - loss: 1.3307 - val_loss: 1.4461
Epoch 64/500
6/6 [==============================] - 3s 539ms/step - loss: 1.3443 - val_loss: 1.3944
Epoch 65/500
6/6 [==============================] - 3s 489ms/step - loss: 1.4256 - val_loss: 1.4174
Epoch 66/500
6/6 [==============================] - 3s 491ms/step - loss: 1.2346 - val_loss: 1.4263
Epoch 67/500
6/6 [==============================] - 3s 489ms/step - loss: 1.5648 - val_loss: 1.4485
Epoch 68/500
6/6 [==============================] - 3s 522ms/step - loss: 1.3661 - val_loss: 1.4223
Epoch 69/500
6/6 [==============================] - 3s 515ms/step - loss: 1.3930 - val_loss: 1.4562
Epoch 70/500
6/6 [==============================] - 3s 479ms/step - loss: 1.3439 - val_loss: 1.5018
Epoch 71/500
6/6 [==============================] - 3s 498ms/step - loss: 1.4951 - val_loss: 1.5331
Epoch 72/500
6/6 [==============================] - 3s 501ms/step - loss: 1.3194 - val_loss: 1.5651
Epoch 73/500
6/6 [==============================] - 3s 515ms/step - loss: 1.3475 - val_loss: 1.4928
Epoch 74/500
6/6 [==============================] - 3s 507ms/step - loss: 1.4108 - val_loss: 1.4353
Epoch 75/500
6/6 [==============================] - 3s 512ms/step - loss: 1.3597 - val_loss: 1.4455
Epoch 76/500
6/6 [==============================] - 3s 502ms/step - loss: 1.3595 - val_loss: 1.4518
Epoch 77/500
6/6 [==============================] - 3s 500ms/step - loss: 1.3726 - val_loss: 1.5113
Epoch 78/500
6/6 [==============================] - 3s 518ms/step - loss: 1.3718 - val_loss: 1.4779
Epoch 79/500
6/6 [==============================] - 3s 522ms/step - loss: 1.3096 - val_loss: 1.4983
Epoch 80/500
6/6 [==============================] - 3s 513ms/step - loss: 1.3204 - val_loss: 1.5021
Epoch 81/500
6/6 [==============================] - 3s 500ms/step - loss: 1.2498 - val_loss: 1.4730
Epoch 82/500
6/6 [==============================] - 3s 513ms/step - loss: 1.2032 - val_loss: 1.4459
Epoch 83/500
6/6 [==============================] - 3s 491ms/step - loss: 1.2305 - val_loss: 1.4384
Epoch 84/500
6/6 [==============================] - 5s 798ms/step - loss: 1.3164 - val_loss: 1.4535
Epoch 84: early stopping
Epoch 1/500
6/6 [==============================] - 11s 736ms/step - loss: 10.0299 - val_loss: 6.2806
Epoch 2/500
6/6 [==============================] - 2s 402ms/step - loss: 7.9998 - val_loss: 5.8609
Epoch 3/500
6/6 [==============================] - 2s 389ms/step - loss: 6.8041 - val_loss: 6.0746
Epoch 4/500
6/6 [==============================] - 2s 390ms/step - loss: 5.8876 - val_loss: 6.7543
Epoch 5/500
6/6 [==============================] - 2s 387ms/step - loss: 5.6444 - val_loss: 5.4325
Epoch 6/500
6/6 [==============================] - 2s 387ms/step - loss: 5.3476 - val_loss: 5.6896
Epoch 7/500
6/6 [==============================] - 2s 402ms/step - loss: 4.9187 - val_loss: 5.6210
Epoch 8/500
6/6 [==============================] - 2s 384ms/step - loss: 4.5355 - val_loss: 5.0421
Epoch 9/500
6/6 [==============================] - 2s 389ms/step - loss: 4.5632 - val_loss: 4.4765
Epoch 10/500
6/6 [==============================] - 2s 393ms/step - loss: 4.2237 - val_loss: 3.7715
Epoch 11/500
6/6 [==============================] - 2s 405ms/step - loss: 3.9522 - val_loss: 3.8725
Epoch 12/500
6/6 [==============================] - 2s 402ms/step - loss: 4.2582 - val_loss: 3.8588
Epoch 13/500
6/6 [==============================] - 2s 402ms/step - loss: 3.8783 - val_loss: 3.4570
Epoch 14/500
6/6 [==============================] - 2s 399ms/step - loss: 3.5164 - val_loss: 3.2688
Epoch 15/500
6/6 [==============================] - 2s 396ms/step - loss: 3.5956 - val_loss: 3.1614
Epoch 16/500
6/6 [==============================] - 2s 399ms/step - loss: 3.5656 - val_loss: 3.0257
Epoch 17/500
6/6 [==============================] - 2s 393ms/step - loss: 3.6226 - val_loss: 2.9701
Epoch 18/500
6/6 [==============================] - 2s 405ms/step - loss: 3.6260 - val_loss: 2.7479
Epoch 19/500
6/6 [==============================] - 2s 387ms/step - loss: 3.3953 - val_loss: 2.7573
Epoch 20/500
6/6 [==============================] - 2s 390ms/step - loss: 3.4222 - val_loss: 3.0039
Epoch 21/500
6/6 [==============================] - 2s 390ms/step - loss: 3.5564 - val_loss: 2.9213
Epoch 22/500
6/6 [==============================] - 2s 402ms/step - loss: 3.3382 - val_loss: 2.9401
Epoch 23/500
6/6 [==============================] - 2s 399ms/step - loss: 3.1911 - val_loss: 2.9853
Epoch 24/500
6/6 [==============================] - 2s 390ms/step - loss: 3.4135 - val_loss: 3.1406
Epoch 25/500
6/6 [==============================] - 2s 396ms/step - loss: 3.2706 - val_loss: 2.9686
Epoch 26/500
6/6 [==============================] - 2s 396ms/step - loss: 3.1576 - val_loss: 3.0537
Epoch 27/500
6/6 [==============================] - 2s 393ms/step - loss: 3.0683 - val_loss: 2.8195
Epoch 28/500
6/6 [==============================] - 2s 396ms/step - loss: 3.1564 - val_loss: 2.6125
Epoch 29/500
6/6 [==============================] - 2s 387ms/step - loss: 3.0087 - val_loss: 2.4908
Epoch 30/500
6/6 [==============================] - 2s 396ms/step - loss: 3.2423 - val_loss: 2.3173
Epoch 31/500
6/6 [==============================] - 2s 402ms/step - loss: 2.9986 - val_loss: 2.2997
Epoch 32/500
6/6 [==============================] - 2s 396ms/step - loss: 3.0172 - val_loss: 2.3600
Epoch 33/500
6/6 [==============================] - 2s 396ms/step - loss: 3.0103 - val_loss: 2.4282
Epoch 34/500
6/6 [==============================] - 2s 387ms/step - loss: 2.9529 - val_loss: 2.3171
Epoch 35/500
6/6 [==============================] - 2s 407ms/step - loss: 3.2229 - val_loss: 2.2733
Epoch 36/500
6/6 [==============================] - 3s 412ms/step - loss: 3.0528 - val_loss: 2.3090
Epoch 37/500
6/6 [==============================] - 2s 390ms/step - loss: 3.1526 - val_loss: 2.2913
Epoch 38/500
6/6 [==============================] - 2s 387ms/step - loss: 3.0123 - val_loss: 2.2020
Epoch 39/500
6/6 [==============================] - 2s 396ms/step - loss: 2.9905 - val_loss: 2.2526
Epoch 40/500
6/6 [==============================] - 2s 399ms/step - loss: 2.8744 - val_loss: 2.2266
Epoch 41/500
6/6 [==============================] - 2s 391ms/step - loss: 2.9694 - val_loss: 2.1861
Epoch 42/500
6/6 [==============================] - 2s 396ms/step - loss: 2.8970 - val_loss: 2.2105
Epoch 43/500
6/6 [==============================] - 2s 405ms/step - loss: 2.7414 - val_loss: 2.1924
Epoch 44/500
6/6 [==============================] - 2s 393ms/step - loss: 3.0568 - val_loss: 2.1893
Epoch 45/500
6/6 [==============================] - 2s 381ms/step - loss: 2.7749 - val_loss: 2.1695
Epoch 46/500
6/6 [==============================] - 2s 396ms/step - loss: 2.8294 - val_loss: 2.1599
Epoch 47/500
6/6 [==============================] - 2s 390ms/step - loss: 2.8132 - val_loss: 2.1815
Epoch 48/500
6/6 [==============================] - 2s 402ms/step - loss: 2.8407 - val_loss: 2.1830
Epoch 49/500
6/6 [==============================] - 3s 412ms/step - loss: 2.8782 - val_loss: 2.2002
Epoch 50/500
6/6 [==============================] - 2s 393ms/step - loss: 2.6921 - val_loss: 2.2208
Epoch 51/500
6/6 [==============================] - 3s 456ms/step - loss: 2.7181 - val_loss: 2.2152
Epoch 52/500
6/6 [==============================] - 3s 443ms/step - loss: 2.7733 - val_loss: 2.2149
Epoch 53/500
6/6 [==============================] - 3s 412ms/step - loss: 2.7121 - val_loss: 2.2123
Epoch 54/500
6/6 [==============================] - 3s 446ms/step - loss: 3.0670 - val_loss: 2.1871
Epoch 55/500
6/6 [==============================] - 3s 431ms/step - loss: 2.8622 - val_loss: 2.1266
Epoch 56/500
6/6 [==============================] - 2s 369ms/step - loss: 2.9914 - val_loss: 2.1592
Epoch 57/500
6/6 [==============================] - 3s 432ms/step - loss: 2.8801 - val_loss: 2.1134
Epoch 58/500
6/6 [==============================] - 2s 406ms/step - loss: 2.7223 - val_loss: 2.1492
Epoch 59/500
6/6 [==============================] - 2s 374ms/step - loss: 2.7111 - val_loss: 2.1409
Epoch 60/500
6/6 [==============================] - 2s 397ms/step - loss: 2.7166 - val_loss: 2.1570
Epoch 61/500
6/6 [==============================] - 2s 384ms/step - loss: 2.6225 - val_loss: 2.1564
Epoch 62/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6955 - val_loss: 2.1512
Epoch 63/500
6/6 [==============================] - 2s 396ms/step - loss: 2.5693 - val_loss: 2.1330
Epoch 64/500
6/6 [==============================] - 3s 434ms/step - loss: 2.8295 - val_loss: 2.1025
Epoch 65/500
6/6 [==============================] - 2s 365ms/step - loss: 2.7576 - val_loss: 2.1521
Epoch 66/500
6/6 [==============================] - 2s 390ms/step - loss: 2.7729 - val_loss: 2.1913
Epoch 67/500
6/6 [==============================] - 2s 393ms/step - loss: 2.6016 - val_loss: 2.1879
Epoch 68/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5979 - val_loss: 2.1137
Epoch 69/500
6/6 [==============================] - 2s 396ms/step - loss: 2.7126 - val_loss: 2.1303
Epoch 70/500
6/6 [==============================] - 2s 390ms/step - loss: 2.5455 - val_loss: 2.1983
Epoch 71/500
6/6 [==============================] - 2s 392ms/step - loss: 2.7300 - val_loss: 2.1468
Epoch 72/500
6/6 [==============================] - 3s 430ms/step - loss: 2.6476 - val_loss: 2.0881
Epoch 73/500
6/6 [==============================] - 2s 400ms/step - loss: 2.5673 - val_loss: 2.2630
Epoch 74/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5633 - val_loss: 2.3258
Epoch 75/500
6/6 [==============================] - 2s 399ms/step - loss: 2.7592 - val_loss: 2.3151
Epoch 76/500
6/6 [==============================] - 2s 393ms/step - loss: 2.7048 - val_loss: 2.2497
Epoch 77/500
6/6 [==============================] - 2s 390ms/step - loss: 2.3934 - val_loss: 2.1902
Epoch 78/500
6/6 [==============================] - 2s 396ms/step - loss: 2.4712 - val_loss: 2.1684
Epoch 79/500
6/6 [==============================] - 2s 402ms/step - loss: 2.5838 - val_loss: 2.1529
Epoch 80/500
6/6 [==============================] - 2s 390ms/step - loss: 2.8386 - val_loss: 2.1860
Epoch 81/500
6/6 [==============================] - 2s 399ms/step - loss: 2.5576 - val_loss: 2.1339
Epoch 82/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5192 - val_loss: 2.1020
Epoch 83/500
6/6 [==============================] - 2s 383ms/step - loss: 2.6917 - val_loss: 2.1269
Epoch 84/500
6/6 [==============================] - 2s 387ms/step - loss: 2.5673 - val_loss: 2.1247
Epoch 85/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6316 - val_loss: 2.1411
Epoch 86/500
6/6 [==============================] - 2s 414ms/step - loss: 2.4855 - val_loss: 2.1826
Epoch 87/500
6/6 [==============================] - 2s 399ms/step - loss: 2.5176 - val_loss: 2.2080
Epoch 88/500
6/6 [==============================] - 2s 384ms/step - loss: 2.5566 - val_loss: 2.1476
Epoch 89/500
6/6 [==============================] - 2s 393ms/step - loss: 2.4796 - val_loss: 2.1838
Epoch 90/500
6/6 [==============================] - 2s 390ms/step - loss: 2.4382 - val_loss: 2.0925
Epoch 91/500
6/6 [==============================] - 3s 441ms/step - loss: 2.5248 - val_loss: 2.0865
Epoch 92/500
6/6 [==============================] - 3s 443ms/step - loss: 2.5292 - val_loss: 2.0802
Epoch 93/500
6/6 [==============================] - 3s 408ms/step - loss: 2.6416 - val_loss: 2.0762
Epoch 94/500
6/6 [==============================] - 2s 393ms/step - loss: 2.3843 - val_loss: 2.0787
Epoch 95/500
6/6 [==============================] - 3s 423ms/step - loss: 2.3888 - val_loss: 2.0611
Epoch 96/500
6/6 [==============================] - 2s 393ms/step - loss: 2.6759 - val_loss: 2.1151
Epoch 97/500
6/6 [==============================] - 2s 387ms/step - loss: 2.4557 - val_loss: 2.0788
Epoch 98/500
6/6 [==============================] - 3s 440ms/step - loss: 2.4592 - val_loss: 2.0400
Epoch 99/500
6/6 [==============================] - 3s 437ms/step - loss: 2.3528 - val_loss: 2.0325
Epoch 100/500
6/6 [==============================] - 3s 443ms/step - loss: 2.3535 - val_loss: 2.0148
Epoch 101/500
6/6 [==============================] - 3s 430ms/step - loss: 2.6083 - val_loss: 2.0079
Epoch 102/500
6/6 [==============================] - 3s 444ms/step - loss: 2.4150 - val_loss: 1.9917
Epoch 103/500
6/6 [==============================] - 2s 361ms/step - loss: 2.5875 - val_loss: 2.0394
Epoch 104/500
6/6 [==============================] - 2s 396ms/step - loss: 2.6018 - val_loss: 2.0633
Epoch 105/500
6/6 [==============================] - 2s 389ms/step - loss: 2.3862 - val_loss: 2.0270
Epoch 106/500
6/6 [==============================] - 2s 396ms/step - loss: 2.5674 - val_loss: 2.0605
Epoch 107/500
6/6 [==============================] - 2s 374ms/step - loss: 2.4243 - val_loss: 2.0424
Epoch 108/500
6/6 [==============================] - 2s 396ms/step - loss: 2.4600 - val_loss: 2.0279
Epoch 109/500
6/6 [==============================] - 2s 377ms/step - loss: 2.4665 - val_loss: 2.0110
Epoch 110/500
6/6 [==============================] - 2s 389ms/step - loss: 2.5692 - val_loss: 2.0907
Epoch 111/500
6/6 [==============================] - 2s 402ms/step - loss: 2.4805 - val_loss: 2.0685
Epoch 112/500
6/6 [==============================] - 2s 397ms/step - loss: 2.5106 - val_loss: 2.0451
Epoch 113/500
6/6 [==============================] - 2s 386ms/step - loss: 2.4530 - val_loss: 2.0344
Epoch 114/500
6/6 [==============================] - 2s 392ms/step - loss: 2.4646 - val_loss: 2.0107
Epoch 115/500
6/6 [==============================] - 3s 447ms/step - loss: 2.5858 - val_loss: 1.9901
Epoch 116/500
6/6 [==============================] - 2s 374ms/step - loss: 2.5665 - val_loss: 2.0009
Epoch 117/500
6/6 [==============================] - 3s 434ms/step - loss: 2.3654 - val_loss: 1.9818
Epoch 118/500
6/6 [==============================] - 2s 377ms/step - loss: 2.4967 - val_loss: 2.0197
Epoch 119/500
6/6 [==============================] - 2s 390ms/step - loss: 2.6798 - val_loss: 2.0174
Epoch 120/500
6/6 [==============================] - 2s 390ms/step - loss: 2.3989 - val_loss: 2.0133
Epoch 121/500
6/6 [==============================] - 2s 383ms/step - loss: 2.4209 - val_loss: 2.0556
Epoch 122/500
6/6 [==============================] - 2s 399ms/step - loss: 2.5805 - val_loss: 2.0375
Epoch 123/500
6/6 [==============================] - 2s 396ms/step - loss: 2.5146 - val_loss: 2.0161
Epoch 124/500
6/6 [==============================] - 2s 377ms/step - loss: 2.2460 - val_loss: 2.0574
Epoch 125/500
6/6 [==============================] - 2s 393ms/step - loss: 2.4696 - val_loss: 2.0866
Epoch 126/500
6/6 [==============================] - 2s 383ms/step - loss: 2.2042 - val_loss: 2.1058
Epoch 127/500
6/6 [==============================] - 2s 374ms/step - loss: 2.4027 - val_loss: 2.0825
Epoch 128/500
6/6 [==============================] - 3s 399ms/step - loss: 2.6062 - val_loss: 2.0867
Epoch 129/500
6/6 [==============================] - 2s 380ms/step - loss: 2.3816 - val_loss: 2.0163
Epoch 130/500
6/6 [==============================] - 2s 393ms/step - loss: 2.4744 - val_loss: 2.0451
Epoch 131/500
6/6 [==============================] - 2s 393ms/step - loss: 2.5002 - val_loss: 2.0774
Epoch 132/500
6/6 [==============================] - 2s 393ms/step - loss: 2.4241 - val_loss: 2.0511
Epoch 133/500
6/6 [==============================] - 2s 396ms/step - loss: 2.4562 - val_loss: 2.0736
Epoch 134/500
6/6 [==============================] - 2s 393ms/step - loss: 2.4903 - val_loss: 2.0825
Epoch 135/500
6/6 [==============================] - 2s 399ms/step - loss: 2.2260 - val_loss: 2.0881
Epoch 136/500
6/6 [==============================] - 2s 402ms/step - loss: 2.2589 - val_loss: 2.0462
Epoch 137/500
6/6 [==============================] - 3s 553ms/step - loss: 2.5452 - val_loss: 2.1046
Epoch 137: early stopping
Epoch 1/500
6/6 [==============================] - 36s 839ms/step - loss: 3.8495 - val_loss: 2.0275
Epoch 2/500
6/6 [==============================] - 3s 480ms/step - loss: 3.0978 - val_loss: 2.1857
Epoch 3/500
6/6 [==============================] - 3s 485ms/step - loss: 2.7500 - val_loss: 2.2092
Epoch 4/500
6/6 [==============================] - 3s 458ms/step - loss: 2.4147 - val_loss: 2.2556
Epoch 5/500
6/6 [==============================] - 3s 499ms/step - loss: 2.3354 - val_loss: 2.3570
Epoch 6/500
6/6 [==============================] - 3s 518ms/step - loss: 2.3358 - val_loss: 2.3474
Epoch 7/500
6/6 [==============================] - 3s 411ms/step - loss: 2.1514 - val_loss: 2.4214
Epoch 8/500
6/6 [==============================] - 3s 538ms/step - loss: 2.1058 - val_loss: 2.2842
Epoch 9/500
6/6 [==============================] - 3s 531ms/step - loss: 2.0881 - val_loss: 2.2195
Epoch 10/500
6/6 [==============================] - 3s 540ms/step - loss: 1.9913 - val_loss: 2.1579
Epoch 11/500
6/6 [==============================] - 3s 470ms/step - loss: 1.8933 - val_loss: 2.1901
Epoch 12/500
6/6 [==============================] - 3s 493ms/step - loss: 1.8902 - val_loss: 2.1770
Epoch 13/500
6/6 [==============================] - 3s 499ms/step - loss: 1.8652 - val_loss: 2.2916
Epoch 14/500
6/6 [==============================] - 3s 491ms/step - loss: 1.7773 - val_loss: 2.2418
Epoch 15/500
6/6 [==============================] - 3s 543ms/step - loss: 1.8376 - val_loss: 2.0604
Epoch 16/500
6/6 [==============================] - 3s 493ms/step - loss: 1.8046 - val_loss: 2.1412
Epoch 17/500
6/6 [==============================] - 3s 527ms/step - loss: 1.7557 - val_loss: 2.1860
Epoch 18/500
6/6 [==============================] - 3s 481ms/step - loss: 1.6431 - val_loss: 2.1475
Epoch 19/500
6/6 [==============================] - 4s 552ms/step - loss: 1.8616 - val_loss: 1.9854
Epoch 20/500
6/6 [==============================] - 3s 537ms/step - loss: 1.7426 - val_loss: 1.8815
Epoch 21/500
6/6 [==============================] - 3s 558ms/step - loss: 1.6734 - val_loss: 1.8227
Epoch 22/500
6/6 [==============================] - 4s 565ms/step - loss: 1.7639 - val_loss: 1.7845
Epoch 23/500
6/6 [==============================] - 3s 555ms/step - loss: 1.6731 - val_loss: 1.7305
Epoch 24/500
6/6 [==============================] - 3s 498ms/step - loss: 1.6735 - val_loss: 1.7640
Epoch 25/500
6/6 [==============================] - 4s 549ms/step - loss: 1.6349 - val_loss: 1.7003
Epoch 26/500
6/6 [==============================] - 4s 534ms/step - loss: 1.7442 - val_loss: 1.6971
Epoch 27/500
6/6 [==============================] - 3s 532ms/step - loss: 1.6380 - val_loss: 1.6940
Epoch 28/500
6/6 [==============================] - 3s 540ms/step - loss: 1.5586 - val_loss: 1.6744
Epoch 29/500
6/6 [==============================] - 3s 490ms/step - loss: 1.5031 - val_loss: 1.6918
Epoch 30/500
6/6 [==============================] - 3s 496ms/step - loss: 1.6260 - val_loss: 1.6932
Epoch 31/500
6/6 [==============================] - 3s 542ms/step - loss: 1.6304 - val_loss: 1.6374
Epoch 32/500
6/6 [==============================] - 4s 553ms/step - loss: 1.6018 - val_loss: 1.6264
Epoch 33/500
6/6 [==============================] - 3s 530ms/step - loss: 1.5496 - val_loss: 1.6033
Epoch 34/500
6/6 [==============================] - 4s 546ms/step - loss: 1.5898 - val_loss: 1.5840
Epoch 35/500
6/6 [==============================] - 4s 574ms/step - loss: 1.5664 - val_loss: 1.5710
Epoch 36/500
6/6 [==============================] - 3s 547ms/step - loss: 1.4181 - val_loss: 1.5616
Epoch 37/500
6/6 [==============================] - 3s 488ms/step - loss: 1.4616 - val_loss: 1.5694
Epoch 38/500
6/6 [==============================] - 3s 526ms/step - loss: 1.4490 - val_loss: 1.5520
Epoch 39/500
6/6 [==============================] - 3s 545ms/step - loss: 1.4618 - val_loss: 1.5391
Epoch 40/500
6/6 [==============================] - 3s 546ms/step - loss: 1.4209 - val_loss: 1.5272
Epoch 41/500
6/6 [==============================] - 3s 487ms/step - loss: 1.5632 - val_loss: 1.5352
Epoch 42/500
6/6 [==============================] - 3s 518ms/step - loss: 1.5052 - val_loss: 1.5667
Epoch 43/500
6/6 [==============================] - 3s 499ms/step - loss: 1.4094 - val_loss: 1.5449
Epoch 44/500
6/6 [==============================] - 3s 504ms/step - loss: 1.5029 - val_loss: 1.5432
Epoch 45/500
6/6 [==============================] - 3s 485ms/step - loss: 1.5680 - val_loss: 1.5855
Epoch 46/500
6/6 [==============================] - 3s 490ms/step - loss: 1.4601 - val_loss: 1.5934
Epoch 47/500
6/6 [==============================] - 3s 499ms/step - loss: 1.4187 - val_loss: 1.6364
Epoch 48/500
6/6 [==============================] - 3s 509ms/step - loss: 1.4143 - val_loss: 1.6036
Epoch 49/500
6/6 [==============================] - 3s 515ms/step - loss: 1.4475 - val_loss: 1.5868
Epoch 50/500
6/6 [==============================] - 3s 499ms/step - loss: 1.3621 - val_loss: 1.5466
Epoch 51/500
6/6 [==============================] - 3s 532ms/step - loss: 1.3418 - val_loss: 1.5049
Epoch 52/500
6/6 [==============================] - 4s 535ms/step - loss: 1.3310 - val_loss: 1.5044
Epoch 53/500
6/6 [==============================] - 4s 537ms/step - loss: 1.3894 - val_loss: 1.4924
Epoch 54/500
6/6 [==============================] - 4s 533ms/step - loss: 1.4941 - val_loss: 1.4827
Epoch 55/500
6/6 [==============================] - 3s 536ms/step - loss: 1.3969 - val_loss: 1.4700
Epoch 56/500
6/6 [==============================] - 3s 533ms/step - loss: 1.3532 - val_loss: 1.4689
Epoch 57/500
6/6 [==============================] - 3s 484ms/step - loss: 1.5459 - val_loss: 1.5045
Epoch 58/500
6/6 [==============================] - 3s 512ms/step - loss: 1.4863 - val_loss: 1.5093
Epoch 59/500
6/6 [==============================] - 3s 506ms/step - loss: 1.4735 - val_loss: 1.5006
Epoch 60/500
6/6 [==============================] - 3s 489ms/step - loss: 1.3647 - val_loss: 1.5024
Epoch 61/500
6/6 [==============================] - 3s 508ms/step - loss: 1.4201 - val_loss: 1.5060
Epoch 62/500
6/6 [==============================] - 3s 510ms/step - loss: 1.4169 - val_loss: 1.5140
Epoch 63/500
6/6 [==============================] - 3s 498ms/step - loss: 1.3858 - val_loss: 1.5293
Epoch 64/500
6/6 [==============================] - 3s 493ms/step - loss: 1.3843 - val_loss: 1.5201
Epoch 65/500
6/6 [==============================] - 3s 513ms/step - loss: 1.4739 - val_loss: 1.5128
Epoch 66/500
6/6 [==============================] - 3s 503ms/step - loss: 1.4552 - val_loss: 1.4873
Epoch 67/500
6/6 [==============================] - 3s 495ms/step - loss: 1.3109 - val_loss: 1.5384
Epoch 68/500
6/6 [==============================] - 3s 501ms/step - loss: 1.4278 - val_loss: 1.5339
Epoch 69/500
6/6 [==============================] - 3s 509ms/step - loss: 1.2426 - val_loss: 1.5372
Epoch 70/500
6/6 [==============================] - 3s 503ms/step - loss: 1.3780 - val_loss: 1.5087
Epoch 71/500
6/6 [==============================] - 3s 509ms/step - loss: 1.3739 - val_loss: 1.4989
Epoch 72/500
6/6 [==============================] - 3s 496ms/step - loss: 1.3250 - val_loss: 1.5709
Epoch 73/500
6/6 [==============================] - 3s 502ms/step - loss: 1.3878 - val_loss: 1.6043
Epoch 74/500
6/6 [==============================] - 3s 507ms/step - loss: 1.3733 - val_loss: 1.5484
Epoch 75/500
6/6 [==============================] - 3s 492ms/step - loss: 1.3218 - val_loss: 1.5305
Epoch 76/500
6/6 [==============================] - 5s 824ms/step - loss: 1.3063 - val_loss: 1.5779
Epoch 76: early stopping

#Get predictions for validation and training sets
validate_pred = np.zeros((nEnsemble, nTest, nDim))
test_pred = np.zeros((nEnsemble, nTest, nDim))
rocks_120_pred = np.zeros((nEnsemble, 120, nDim))
for e in range(nEnsemble):
    model = load_model("ensemble_densenet_8_{}.hdf5".format(e))
    validate_pred[e,:] = model.predict(X_validate)
    test_pred[e,:] = model.predict(X_test)
    rocks_120_pred[e,:] = model.predict(X_120)
    
    K.clear_session()

validate_prediction = np.mean(validate_pred, 0)
test_prediction = np.mean(test_pred, 0)
rocks_120_prediction = np.mean(rocks_120_pred, 0)

#Get MSE
print(mean_squared_error(Y_validate, validate_prediction))
print(mean_squared_error(Y_test, test_prediction))
print(mean_squared_error(Y_120, rocks_120_prediction))

3/3 [==============================] - 2s 189ms/step
3/3 [==============================] - 0s 70ms/step
4/4 [==============================] - 0s 63ms/step
3/3 [==============================] - 2s 157ms/step
3/3 [==============================] - 0s 71ms/step
4/4 [==============================] - 0s 63ms/step
3/3 [==============================] - 2s 236ms/step
3/3 [==============================] - 0s 69ms/step
4/4 [==============================] - 0s 61ms/step
3/3 [==============================] - 3s 236ms/step
3/3 [==============================] - 0s 63ms/step
4/4 [==============================] - 0s 68ms/step
3/3 [==============================] - 2s 228ms/step
3/3 [==============================] - 0s 63ms/step
4/4 [==============================] - 0s 59ms/step
3/3 [==============================] - 2s 212ms/step
3/3 [==============================] - 0s 63ms/step
4/4 [==============================] - 0s 59ms/step
3/3 [==============================] - 2s 216ms/step
3/3 [==============================] - 0s 63ms/step
4/4 [==============================] - 0s 58ms/step
3/3 [==============================] - 2s 220ms/step
3/3 [==============================] - 0s 63ms/step
4/4 [==============================] - 0s 59ms/step
3/3 [==============================] - 2s 197ms/step
3/3 [==============================] - 0s 63ms/step
4/4 [==============================] - 0s 60ms/step
3/3 [==============================] - 2s 157ms/step
3/3 [==============================] - 0s 71ms/step
4/4 [==============================] - 0s 63ms/step
1.3152003843531315
1.4433026833068152
3.0617280031522824

#Get R2
print(r2_score(Y_validate, validate_prediction))
print(r2_score(Y_test, test_prediction))
print(r2_score(Y_120, rocks_120_prediction))

0.7687254736329209
0.7525643698572588
-0.41798612086330167

import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from PIL import Image

images = X_validate
mds_coordinates = validate_prediction

min_val = -2.117904
max_val = 2.64

scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8) 
scaled_images = np.clip(scaled_images, 0, 255)

# Create subplots for different pairs of dimensions
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))

# Pairs of dimensions: (0, 1), (2, 3), (4, 5), (6, 7)
dimension_pairs = [(0, 1), (2, 3), (4, 5), (6, 7)]

for i, ax in enumerate(axes.flatten()):
    dimension_x, dimension_y = dimension_pairs[i]
    
    # Scatter plot of MDS coordinates
    ax.scatter(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y], c='blue', alpha=0.5)
    
    # Function to display resized images
    def plot_resized_image(img, x, y, ax):
        img_copy = Image.fromarray(img)
        img_copy = img_copy.resize((20, 20))  # Resize images to fit within the subplot
        imagebox = OffsetImage(img_copy, zoom=1.0)
        ab = AnnotationBbox(imagebox, (x, y), frameon=False)
        ax.add_artist(ab)
    
    # Plot each image at its respective MDS coordinates
    for j, (x, y) in enumerate(zip(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y])):
        image = scaled_images[j]  # Get the image at index j
        plot_resized_image(image, x, y, ax)
    
    ax.set_xlabel(f'MDS Dimension {dimension_x+1}')
    ax.set_ylabel(f'MDS Dimension {dimension_y+1}')

plt.suptitle(f'Visualization of CNN predicted MDS Dimensions')
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from PIL import Image

images = X_test
mds_coordinates = test_prediction

min_val = -2.117904
max_val = 2.64

scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8) 
scaled_images = np.clip(scaled_images, 0, 255)

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))

dimension_pairs = [(0, 1), (2, 3), (4, 5), (6, 7)]

for i, ax in enumerate(axes.flatten()):
    dimension_x, dimension_y = dimension_pairs[i]
    
    # Scatter plot of MDS coordinates
    ax.scatter(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y], c='blue', alpha=0.5)
    
    # Function to display resized images
    def plot_resized_image(img, x, y, ax):
        img_copy = Image.fromarray(img)
        img_copy = img_copy.resize((20, 20))  # Resize images to fit within the subplot
        imagebox = OffsetImage(img_copy, zoom=1.0)
        ab = AnnotationBbox(imagebox, (x, y), frameon=False)
        ax.add_artist(ab)
    
    # Plot each image at its respective MDS coordinates
    for j, (x, y) in enumerate(zip(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y])):
        image = scaled_images[j]  # Get the image at index j
        plot_resized_image(image, x, y, ax)
    
    ax.set_xlabel(f'MDS Dimension {dimension_x+1}')
    ax.set_ylabel(f'MDS Dimension {dimension_y+1}')

plt.suptitle(f'Visualization of CNN predicted MDS Dimensions')
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from PIL import Image

images = X_120
mds_coordinates = rocks_120_prediction

min_val = -2.117904
max_val = 2.64

scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8) 
scaled_images = np.clip(scaled_images, 0, 255)

# Create subplots for different pairs of dimensions
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))

# Pairs of dimensions: (0, 1), (2, 3), (4, 5), (6, 7)
dimension_pairs = [(0, 1), (2, 3), (4, 5), (6, 7)]

for i, ax in enumerate(axes.flatten()):
    dimension_x, dimension_y = dimension_pairs[i]
    
    # Scatter plot of MDS coordinates
    ax.scatter(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y], c='blue', alpha=0.5)
    
    # Function to display resized images
    def plot_resized_image(img, x, y, ax):
        img_copy = Image.fromarray(img)
        img_copy = img_copy.resize((20, 20))  # Resize images to fit within the subplot
        imagebox = OffsetImage(img_copy, zoom=1.0)
        ab = AnnotationBbox(imagebox, (x, y), frameon=False)
        ax.add_artist(ab)
    
    # Plot each image at its respective MDS coordinates
    for j, (x, y) in enumerate(zip(mds_coordinates[:, dimension_x], mds_coordinates[:, dimension_y])):
        image = scaled_images[j]  # Get the image at index j
        plot_resized_image(image, x, y, ax)
    
    ax.set_xlabel(f'MDS Dimension {dimension_x+1}')
    ax.set_ylabel(f'MDS Dimension {dimension_y+1}')

plt.suptitle(f'Visualization of CNN predicted MDS Dimensions')
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from scipy.stats import pearsonr
from PIL import Image

images = X_validate
mds_cnn_coordinates = validate_prediction  
mds_actual_coordinates = Y_validate 

# Rescale images
min_val = -2.117904
max_val = 2.64
scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8) 
scaled_images = np.clip(scaled_images, 0, 255)

# Calculate Pearson correlation coefficients for each dimension
correlation_coefficients = [pearsonr(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])[0] for i in range(8)]

# Create subplots for each dimension
fig, axes = plt.subplots(4, 2, figsize=(12, 20))
# fig.suptitle("Scatterplots of CNN-predicted dimensions against MDS derived dimensions")
axes = axes.flatten()

for i in range(8):
    ax = axes[i]
    
    ax.scatter(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i], alpha=0.7)
    
    for j, (x_cnn, y_actual) in enumerate(zip(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])):
        plot_resized_image(scaled_images[j], x_cnn, y_actual, ax)
    
    ax.plot(np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
                        max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
            np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
                        max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
            'k--', label='Perfect Prediction')
    
    ax.set_xlabel(f'CNN-predicted Dimension {i+1}')
    ax.set_ylabel(f'MDS-derived Dimension {i+1}')
    ax.legend()
    ax.grid(True)
    
    ax.text(0.5, 0.95, f"r: {correlation_coefficients[i]:.4f}", ha='center', va='center', transform=ax.transAxes, fontsize=8)

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from scipy.stats import pearsonr
from PIL import Image

images = X_test
mds_cnn_coordinates = test_prediction 
mds_actual_coordinates = Y_test  

# Rescale images
min_val = -2.117904
max_val = 2.64
scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8) 
scaled_images = np.clip(scaled_images, 0, 255)

# Calculate Pearson correlation coefficients for each dimension
correlation_coefficients = [pearsonr(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])[0] for i in range(8)]

# Create subplots for each dimension
fig, axes = plt.subplots(4, 2, figsize=(12, 20))
axes = axes.flatten()

for i in range(8):
    ax = axes[i]
    
    ax.scatter(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i], alpha=0.7)
    
    for j, (x_cnn, y_actual) in enumerate(zip(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])):
        plot_resized_image(scaled_images[j], x_cnn, y_actual, ax)
    
    ax.plot(np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
                        max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
            np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
                        max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
            'k--', label='Perfect Prediction')
    
    ax.set_xlabel(f'CNN-predicted Dimension {i+1}')
    ax.set_ylabel(f'MDS-derived Dimension {i+1}')
    ax.legend()
    ax.grid(True)
    
    ax.text(0.5, 0.95, f"r: {correlation_coefficients[i]:.4f}", ha='center', va='center', transform=ax.transAxes, fontsize=8)

plt.tight_layout()
# plt.suptitle("Scatterplots of CNN-predicted dimensions against MDS derived dimensions")
plt.show()

import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
from scipy.stats import pearsonr
from PIL import Image

images = X_120
mds_cnn_coordinates = rocks_120_prediction  
mds_actual_coordinates = Y_120 

# Rescale images
min_val = -2.117904
max_val = 2.64
scaled_images = (images - min_val) / (max_val - min_val)
scaled_images = (scaled_images * 255).astype(np.uint8) 
scaled_images = np.clip(scaled_images, 0, 255)

# Calculate Pearson correlation coefficients for each dimension
correlation_coefficients = [pearsonr(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])[0] for i in range(8)]

# Create subplots for each dimension
fig, axes = plt.subplots(4, 2, figsize=(12, 20))
# fig.suptitle("Scatterplots of CNN-predicted dimensions against MDS derived dimensions")
axes = axes.flatten()

for i in range(8):
    ax = axes[i]
    
    ax.scatter(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i], alpha=0.7)
    
    for j, (x_cnn, y_actual) in enumerate(zip(mds_cnn_coordinates[:, i], mds_actual_coordinates[:, i])):
        plot_resized_image(scaled_images[j], x_cnn, y_actual, ax)
    
    ax.plot(np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
                        max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
            np.linspace(min(mds_actual_coordinates[:, i].min(), mds_cnn_coordinates[:, i].min()),
                        max(mds_actual_coordinates[:, i].max(), mds_cnn_coordinates[:, i].max()), 100),
            'k--', label='Perfect Prediction')
    
    ax.set_xlabel(f'CNN-predicted Dimension {i+1}')
    ax.set_ylabel(f'MDS-derived Dimension {i+1}')
    ax.legend()
    ax.grid(True)
    
    ax.text(0.5, 0.95, f"r: {correlation_coefficients[i]:.4f}", ha='center', va='center', transform=ax.transAxes, fontsize=8)

plt.tight_layout()
plt.show()